SOFT BIOMETRICS: HUMAN IDENTIFICATION USING … · 2020. 10. 7. · SOFT BIOMETRICS: HUMAN IDENTIFICATION USING COMPARATIVE DESCRIPTIONS 1 Soft Biometrics; Human Identiﬁcation using

SOFT BIOMETRICS: HUMAN IDENTIFICATION USING COMPARATIVE DESCRIPTIONS 1

Soft Biometrics; Human Identification usingComparative Descriptions

Daniel A. Reid, Mark S. Nixon, Sarah V. Stevenage

Abstract—Soft biometrics are a new form of biometric identification which use physical or behavioral traits that can be naturallydescribed by humans. Unlike other biometric approaches, this allows identification based solely on verbal descriptions, bridgingthe semantic gap between biometrics and human description. To permit soft biometric identification the description must beaccurate, yet conventional human descriptions comprising of absolute labels and estimations are often unreliable. A novel methodof obtaining human descriptions will be introduced which utilizes comparative categorical labels to describe differences betweensubjects. This innovative approach has been shown to address many problems associated with absolute categorical labels- most critically, the descriptions contain more objective information and have increased discriminatory capabilities. Relativemeasurements of the subjects’ traits can be inferred from comparative human descriptions using the Elo rating system. Theresulting soft biometric signatures have been demonstrated to be robust and allow accurate recognition of subjects. Relativemeasurements can also be obtained from other forms of human representation. This is demonstrated using a support vectormachine to determine relative measurements from gait biometric signatures - allowing retrieval of subjects from video footage byusing human comparisons, bridging the semantic gap.

Index Terms—Soft Biometrics, Human Descriptions, Retrieval, Comparisons, Regression, Gait Biometrics

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1 INTRODUCTION

Traditional biometric techniques identify people usingdistinct physical or behavioral features. These featuresare clearly discriminative although they can rarely bedescribed using linguistic labels. This is known as asemantic gap. This restricts identification to situationsin which the subject’s biometric signature can beobtained and only permits identification of those sub-jects whose biometric signature has previously beenrecorded. Soft biometrics are a new form of biometricidentification which concerns labels that people useto describe each other, like ‘tall’, ‘skinny’ and ‘male’.Although each label can have reduced discriminativecapability, they can be combined for identification [1],[2] and fusion with traditional ‘hard’ biometrics [3],[4]. Dantcheva et al. [5] likens this to obtaining a singleridge of a fingerprint or a small section of the iris.These would not be sufficient to identify a subject butby gathering many small features we are able to builda unique biometric signature.

One of the main advantages of soft biometrics istheir relationship with human description: humansnaturally use soft biometric traits to identify anddescribe each other. This permits identification andretrieval based solely on a human description of thesubject, possibly obtained from an eyewitness.

• D.Reid and M.Nixon are with the School of Electronics and ComputerScience, University of Southampton, Southampton, UK. S.Stevenageis with the School of Psychology, University of Southampton, UK

1. Metropolitan Police Flickr Account

Fig. 1. Surveillance frame displaying common surveil-lance problems1

Though face and gait are the only practical biomet-rics at a distance, in surveillance scenarios they cansuffer from low frame rate and/or resolution. Figure1 shows an example of a typical CCTV video frameshowing looters at the 2011 London riots. It can beobserved that although the picture is at low resolutiona detailed human description of the subjects can stillbe given. In comparison, automatic facial recognitionwould struggle with the low resolution and non-frontal viewpoint. Soft biometric traits can be obtainedfrom the data derived from low quality sensors, in-cluding surveillance cameras. Soft biometrics also re-quire less computation compared to ‘hard’ biometrics,no cooperation from the subject and are non-invasive- making them ideal in surveillance applications.


To allow identification from human descriptions,physical properties must be accurately described.Conventional human descriptions represent an im-portant element of eyewitness evidence, althoughthey can be considered inaccurate and unreliable [6],[7]. They consist of either absolute categorical labels(e.g. ‘tall’) or estimates of human characteristics (e.g.190cm). Previously, categorical labels have been usedin a soft biometric system [1]. Given that humanscan be inaccurate when predicting measurements [8],labels were seen as a more robust method of obtaininghuman descriptions. One major problem associatedwith absolute categorical labels is their highly sub-jective nature. A label’s meaning is based on theperson’s own attributes and their own perceptionof population averages and variation. This can vary,making absolute labels less reliable. Categorical labelsnaturally lack detail, resulting in biometric signatureswhich have poor discriminatory capability. This paperintroduces a new method for obtaining human de-scriptions which exploits the process of making visualcomparisons between subjects.

Comparing the appearance of two subjects is a verynatural process and may be more reliable than the useof absolute labels because comparisons are assignedbased on a specified benchmark resulting in a moreobjective description. We exploit the ease of makingcomparisons to explore a new method to providereliable and robust descriptions.

A set of relative measurements describing the subjectcan be accurately inferred from comparative labels(that are derived from comparing one person withanother, e.g. ‘taller’) and used as a biometric signa-ture. These signatures have been shown to be highlydiscriminative allowing accurate biometric retrieval.

The novelty of this paper is to demonstrate how in-dividuals can be identified from a database of videosbased solely on a verbal comparative description, withparticular contribution of

• Extended analysis of the benefits of comparativelabels over absolute

• Extended description of the derivation of relativemeasures from comparative labels

• Demonstration of capability to learn soft biomet-ric labels and relative measurements from video

• Demonstration of capability to retrieve subjectsfrom a database by verbal description

The remainder of this paper will explore the ef-fectiveness of human comparisons and how they canbe applied to soft biometric recognition and retrieval.Section 2 will investigate related work in psychology,biometrics and computer vision. An introduction tohuman comparisons and the database used through-out this paper will be presented in section 3. Finally,section 4 will explore how to utilize comparisons foridentification. This will include an introduction to theElo ranking system which is used to infer relative

measurements and the identification of subjects fromsoft biometric databases and video footage.

2 RELATED WORK

2.1 Psychology of Human Descriptions

To allow identification from human descriptions, thephysical properties described must be accurate, salientand reliable. Human descriptions generally consistof two types of descriptions: labels and measure-ments. Labels are predominantly used to describeinherently categorical traits like ethnicity and gender,but they can also be used to describe continuous traits,for instance a height description can include ‘short’,‘medium’ and ‘tall’. Estimations of continuous traitsare more commonly described using measurementsdetailing the feature’s length, width or weight. Muchresearch has been conducted into obtaining accuratehuman descriptions due to their importance in manycriminal investigations.

Kuehn studied the descriptions provided by vic-tims in 100 police investigations [9]. Despite theprominence given to faces in eyewitness testimony, itwas discovered that gender, age, height, build, race,weight, complexion, and hair color were mentionedover 70% of the time, whilst facial features were rarelymentioned. Similarity, MacLeod et al. investigated theprominence and reliability of whole body descriptors[10]. 13 of the most reliable body descriptors wereidentified from a total of 1238, the most reliable beingthin-fat, short-tall, thin-thick legs, slim-barrel chestand short-long legs.

Yuille and Cutshall [8] showed that estimates ofheight, weight and age were incorrect 50% of thetime based on 95 cases (considered accurate if within2 inches, 5 pounds and 2 years respectively of theactual measurement). Inaccurate estimates have beenattributed to an own anchor effect, where the wit-ness’s own characteristics were used as a point fromwhich to judge the suspect [11]. It was also found thatdescriptions tended to show a regression to the mean,or to what the witness estimated as the mean. Shorterpeople were estimated as taller than they really were,and vice versa. This was thought to occur due to thewitness shying away from extreme judgments [7].

The current paper introduces a new form of humandescription for soft biometrics which was systemat-ically designed to avoid the limitations of absolutelabels and continuous estimations whilst building onthe analysis of the content of human descriptions.

2.2 Human Descriptions and Soft Biometrics

Soft biometrics can be split into three distinct cate-gories: bolstering of hard biometrics utilizing mea-sured soft biometric traits, identification using mea-sured soft biometric traits and identification usingverbal descriptions of soft biometric traits. This paper


Fig. 2. The relationship between pixel height andabsolute labels

presents an identification technique which utilizesverbal descriptions of soft biometric traits and assuch this section will examine how verbal descriptionshave previously been used in soft biometrics. For asurvey of soft biometrics as a whole please refer to[12].

There are two distinct methods for obtaining infor-mation about soft biometric features. The first auto-matically identifies features from data describing thesubject, most commonly in the form of imagery. Thesecond aims to generate biometric information fromverbal descriptions of the subject.

Samangooei and Nixon [1] developed a soft bio-metric system which identifies subjects from videofootage (Soton gait database [13]) based solely ona verbal human description. This description wascomposed of 23 absolute categorical labels which werechosen to be universal, distinct, easily discernible ata distance and largely permanent. The selected softbiometric traits featured both intrinsically categoricalattributes, like hair color, and characteristics generallyassociated with value metrics, like height - both weredescribed using absolute labels.

Initially 959 descriptions of the 115 subjects fromthe Soton gait database were obtained and used tobuild a database of soft biometric feature vectorswhich described the given descriptions. Initial anal-ysis of the descriptions showed that the categoricallabels used to describe the subjects were unreliable,especially when describing traits generally associatedwith value metrics. Figure 2 shows the relationshipbetween the height of the subjects (obtained fromthe video footage and represented in pixels) and themedian absolute height label used to describe thesubjects. Large overlaps between the short, mediumand tall labels were observed resulting in a statisticallysignificant (p < 0.0001) Pearson’s correlation of 0.71.This incorrectness between actual and labeled heightis due to the categorical and highly subjective natureof the labels.

Fig. 3. Retrieval accuracy of absolute descriptionsfrom a soft biometric database

The database of soft biometric feature vectors wasused by the authors to assess the discriminatorypower of the descriptions. Each subject’s feature vec-tor consisted of the most commonly used label to de-scribe a subject’s soft biometric trait (on average eachsubject was described by 8 individual annotators).Recognition experiments were conducted by retriev-ing subjects from the database to assess the unique-ness of each subject’s soft biometric feature vectorand the variance between multiple descriptions ofthe same subject. A leave-one-out validation approachwas used to evaluate the recognition performance.The probe, which was used to query the database,was formed from a single verbal description of thesubject given by a single annotator. The remainingdescriptions of the subject were averaged and usedas the gallery, the feature vector within the databasebeing searched. Figure 3 shows the results. The rank1 retrieval performance (i.e. the recognition accuracy)was found to be 48%. Retrieval performance increasedto 90% at rank 15. Subject interference [5] is a knownproblem when using labels and occurs when two sub-jects are indistinguishable from each other due to thelimited number of labels available. This obviously hasa drastic effect when attempting to identify a subjectand would explain the poor recognition results. Thishighlights the lack of distinctiveness between subjectsdue to the limited information conveyed using cate-gorical labels. As such, absolute labels can be used torecognize people but are limited in accuracy leadingto a limited recognition capability.

Denman et al. [14] used soft biometric traits toidentify people using previous observations or humandescriptions when traditional biometrics are unavail-able. The height and colour of the torso, legs, and headare used to model subjects. Identifying these threebody components is done by first locating the personusing background segmentation and then analysingthe colour of moving pixels in each row. Large colourdifferences can often be found between the head, torso


and legs due to clothing that can be easily identi-fied by examining colour gradients. The PETS 2006surveillance database was used to test the system.This dataset features four cameras monitoring a trainstation: four recordings of 25 people were obtained.The system achieved an equal error rate of 6.1% whenidentifying individuals from previous observations.Unfortunately the performance of identification uti-lizing verbal descriptions was not detailed.

2.3 Relative InformationRelative information has recently been explored toimprove human descriptions of objects within im-ages. Several techniques have exploited similaritiesbetween objects as a form of description. Kumar etal. [15] have explored similarities between faces toidentify and explain facial attributes. The developed‘simile classifiers’ recognize similarities between a face(or regions of a face) and a set of specific refer-ence subjects. This allows descriptions such as ‘lipslike Barack Obama’ or ‘a nose like Owen Wilson’. Theadvantage of this system is the ability to producedescriptions of features which are generally hard todescribe. Wang et al. [16] exploits similarities betweenobjects to allow recognition with few or no examples.Descriptions such as ‘a zebra is similar to a horse inshape and a crosswalk in texture’, allows the approachto identify a zebra with no training examples. Ex-ploiting descriptions of similarity between objects hasbeen shown to improve recognition of objects withinimages with few training examples. Both of thesetechniques utilize relative information to improvedescriptions, although they differ significantly fromour approach. Similarity between reference subjectsor other objects provides a method of description,whereas the comparing of subjects provides an or-dering based on the specific trait being compared.Although different, these techniques show the benefitsof relative information especially when describingfeatures or attributes which are normally difficult tocommunicate.

Image descriptions have been further improved bydetermining order based on the strength of a specificattribute, allowing such comparisons as ‘lions are largerthan dogs’ [17]. Given a set of images and a partialset of comparisons detailing the relative strength ofa certain attribute, the technique determines a com-plete ordering of the images. This was approachedas an optimization problem where the comparisonswere treated as constraints. A ranking support vectormachine was used to determine a ranking functionwhich fitted a weight vector to maximize the numberof constraints satisfied - this was based on rankingalgorithms used within search engines [18]. The rank-ing function could then be used to determine theordering between all of the images. Zero-shot learningfrom relationships was introduced based on this or-dering approach, allowing previously unseen objects

to be identified based on comparisons with observedobjects. The zero-shot learning results show that therelative descriptions convey stronger discriminatorypower compared to binary descriptions.

3 HUMAN COMPARISONS FOR SOFT BIO-METRICS

In this section we detail our new approach that usescomparisons to address the difficulties associated withconventional human descriptions.

3.1 Human Comparison DatabaseThe method used to obtain descriptions from an ob-server is an important consideration when exploringa new form of human description. In the case ofhuman comparisons the practical limitations of hu-man memory and the ability of humans to comparebodily attributes must be considered and explored.An experiment was designed to answer the followingquestions:

• Do relative measurements provide more discrim-inatory information than absolute labels?

• Are the resulting relative measurements highlycorrelated with the subject’s physical attributes?

• Is the developed method of obtaining humancomparisons practical?

Although descriptive, a single comparison betweena suspect and another person will only explain the dif-ferences between the two. Thus, the inferred physicaltraits of the suspect will depend on the subject theywere compared to. Multiple comparisons must beavailable to infer a more robust description, with eachcomparison allowing the description of the suspectto be refined. A practical method of obtaining com-parisons, between a target subject (representing thesuspect in application settings) and multiple subjects,is to present videos of the subjects to the annota-tor. This permits multiple comparisons with minimalequipment and personnel. To validate this approachthe experiment will present videos of subjects fromthe Soton gait database [13] to the annotator. The gaitdatabase includes videos of 100 people walking in aplane normal to the view of the camera. Previouslyabsolute categorical labels had been collected for thesame database [1] - allowing comparisons to be drawnbetween the two forms of description.

Comparisons were gathered using the websiteshown in figure 4. The website was designed to allowvideos of both the subject and target to be presentedto the annotator simultaneously. This allows users tomake direct comparisons without memory demandsor uncertainties concerning the scale of the videos.Drop-down boxes for each trait allowed users todescribe how the subject differed from the target.The chosen label was emphasized by constructing asentence explaining the given annotation - ensuring


Fig. 4. Comparative label collection

the annotator was comparing the subject to the targetinstead of vice versa. Eyewitness descriptions canbe influenced by providing a default answer to aquestion, this is known as anchoring [19]. To avoidanchoring, all drop down boxes were initially void -forcing a response from the annotator.

The users were asked to compare two subjectswhilst both were visible. Five subjects were comparedto a single target - this simulates the idea of comparinga selection of subjects against a suspect. A singlehuman comparison consists of 16 trait comparisons(shown in table 1), each using one of five compar-ative labels. The traits chosen were primarily basedon MacLeod’s work [10]. It can be observed thatthree traits (gender, ethnicity and skin color) wereannotated using absolute labels. These three traits areunsuited to comparative annotations, either due tothe inherently categorical nature of the trait or thelack of a suitable comparison criterion. These abso-lute annotations are not considered when analyzingthe comparative annotations and are used only forrecognition and retrieval.

The 100 subjects from the Soton gait database wereassigned as one of either 20 targets or 80 subjects.Subjects were assigned to users so as to maximize thenumber of descriptions comparing different subjectsand targets.

Performing comparisons between a large group ofsubjects and a small group of targets also allowedinference of annotations between subjects. If two sub-jects were both compared against the same target thenthe comparison between the two subjects could be in-ferred, reducing the number of comparisons required.Inferring comparisons does, however, introduce er-rors. If two subjects are both labeled as ‘taller’ than thetarget, the inferred comparison would be ‘same’. Thelikelihood is that the subjects are not the same heightand we are losing resolution with this assumption.Although lacking precision, this approach allowed usto fully exploit the comparisons we obtained from

limited experiments.In the work reported here, there have been 558

comparisons between subjects and targets collectedfrom 57 annotators.

3.2 Database analysisEach soft biometric trait comparison is comprised ofa single categorical label taken from a set of fiveordered labels (see table 1). Each of the five labels areassigned a value, ranging from -2 to 2, based on theirorder such that -2 represents a ‘much less’ comparisonand +2 a ‘much more’. A comparison, Cst, between asubject, s, and a target, t, can be described as follows:

Cst ∈ {−2,−1, 0, 1, 2} (1)

The comparative annotations were compared withthe absolute categorical labels gathered by Saman-gooei and Nixon [1]. This comparison between anno-tation techniques will not show which is better, onlyhow much each technique differs from the other. Todetermine the difference between the descriptions thecomparative label is compared against the absolutelabels used to annotate the subject and target. Ifthe absolute labels differ and the comparative labelreflects this difference the annotations are recordedas concurring - for example if the target and subjectwere labeled as ‘short’ and ‘tall’ respectively andthe comparative descriptor provided was ‘taller’, wewould consider both annotations as concurring. Theabsolute annotations obviously lack detail; two peoplelabeled as ‘tall’ are unlikely to be exactly the sameheight. Thus, small differences can be described usingcomparative annotations but not absolute labels. Inthe case of both the subject and target having thesame absolute label, the similarity of the comparativeannotation cannot be determined. In this case thecomparative annotation was recorded as concurring- this ensures we do not overestimate the differencebetween absolute and comparative annotations. Suchthat:

accuracy(At, As, Cst) =

1 As < At and Cst < 01 As > At and Cst > 01 As = At0 otherwise

(2)Where A is a value representing an ordered absolutelabel and Cst is a comparison between a target, t,and a subject, s. It was found that the comparativeannotations differ from the absolute 17% of the time.This does not necessarily mean that the comparativeannotations are better - just that they are different tothe absolute labels on 17% of occasions

Figure 5 shows the average difference betweenabsolute and comparative annotations for each trait.The F-ratios, derived by ANOVA analysis, presentedwithin [1] clearly show that absolute labels describesome features better than others. Large differencesbetween absolute and comparative labels for traits


Trait Description Type LabelsArm Length Comparative [Much Shorter, Shorter, Same, Longer, Much Longer]Arm Thickness Comparative [Much Thinner, Thinner, Same, Thicker, Much Thicker]Chest Comparative [Much Smaller, Smaller, Same, Bigger, Much Bigger]Figure Comparative [Much Smaller, Smaller, Same, Larger, Much Larger]Height Comparative [Much Shorter, Shorter, Same, Taller, Much Taller]Hips Comparative [Much Narrower, Narrower, Same, Broader, Much Broader]Leg Length Comparative [Much Shorter, Shorter, Same, Longer, Much Longer]Leg Thickness Comparative [Much Thinner, Thinner, Same, Thicker, Much Thicker]Muscle Build Comparative [Much Leaner, Leaner, Same, More Muscular, Much More Muscular]Shoulder Shape Comparative [More Square, Same, More Rounded]Weight Comparative [Much Thinner, Thinner, Same, Fatter, Much Fatter]Age Comparative [Much Younger, Younger, Same, Older, Much Older]Ethnicity Absolute [European, Middle Eastern, Far Eastern, Black, Mixed, Other]Gender Absolute [Female, Male]Skin Color Absolute [White, Tanned, Oriental, Black]Hair Color Comparative [Much Lighter, Lighter, Same, Darker, Much Darker]Hair Length Comparative [Much Shorter, Shorter, Same, Longer, Much Longer]Neck Length Comparative [Much Shorter, Shorter, Same, Longer, Much Longer]Neck Thickness Comparative [Much Thinner, Thinner, Same, Thicker, Much Thicker]

TABLE 1Soft traits used to compare subjects

Fig. 5. The average difference of each comparativetrait and categorical annotation

demonstrated to be difficult to describe using absolutelabels would be indicative of potential improvementswhen using comparative labels. It can be seen thatcomparative annotations of arm length (one of thehardest traits to explain categorically) differs on av-erage by 30% compared to absolute labels. Giventhe inaccuracy of absolute labels for this trait, thedifference suggests that the comparative annotationscontain more detailed information. Conversely, smalldifferences for traits which were accurately describedusing absolute annotations, for example hair length,demonstrate that the trait is reliably described usingboth approaches. It can be observed that the differencebetween absolute and comparative annotations are onaverage 5% in respect to hair length, which shows thatthe comparisons are largely the same as the successfuldata obtained from the absolute annotations.

4 HUMAN IDENTIFICATION USING COMPAR-ISONS

Comparisons have been introduced as a more ro-bust method for gathering descriptions, but we must

consider how they can be applied to identificationapplications. There are two separate identificationsituations we will consider in this section. The firstrecognizes a subject from a database of soft biometricsignatures. The second retrieves a subject from adatabase of videos. In both instances the probe featurevector will be constructed from verbal comparativedescriptions.

The first stage in both applications is to convert thecomparative descriptions to relative measurementswhich can be used as a biometric signature. Thisis described in section 4.1. Sections 4.2 and 4.3 de-scribe identification of subject within a soft biometricdatabase and automatic retrieval of a subject fromvideo footage respectively.

4.1 Relative Measurements

Comparisons are inherently relative; each subject isdescribed using another subject as a benchmark. Com-parative annotations must be anchored to conveymeaningful subject invariant information. The result-ing value is a relative measurement, providing ameasurement of a specific trait in relation to the restof the population. This can be used as a biometricfeature allowing retrieval and recognition based on asubject’s relative trait measurements.

4.1.1 Law of Comparative JudgmentIn 1927, Thurstone introduced the law of comparativejudgment [20], allowing the underlying strength of anentity’s attribute (also known as the entity’s quality)to be determined from pairwise comparisons. Themodel allowed the calculation of quality scores fora single pair of entities and was later extended todetermine the quality of more than two entities. Thur-stone’s model employed Gaussian distributions tomodel pairwise comparisons. It was assumed that anindividual’s judgment of an entity’s quality could beconsidered as a Gaussian random variable, modelingthe subjective nature of assessing ‘quality’. Therefore,


the entity’s quality score could be modeled by themean quality of the resulting Gaussian distribution.This section will introduce Thurstone’s case V model(which assumes no correlation between comparedentities and equal variance of 0.5).

Given two entities, i and j, each considered as aGaussian random variable. Thurstone states that anindividual will compare the entities by drawing tworealizations from the entities’ quality distributions.The probability that the individual will choose i overj, P (i > j), is dependent on whether their realizationof i is greater than their realization of j, such thatP (i > j) = P (i − j > 0). Given that i − j is thedifference between two Gaussian random variables,i − j is also a Gaussian random variable, whereP (i− j > 0) can be calculated using:

P (i− j > 0) = Φ (µi − µj) (3)

where µ is the mean of the corresponding Gaussianrandom variable and Φ(x) is the standard normal cu-mulative distribution function (CDF). Once P (i > j)is determined this can be inverted to find µi − µj :

µi − µj = Φ−1(P (i > j)) (4)

where Φ−1 is the inverse of the standard normalCDF. Equation 4 is known as Thurstone’s law ofcomparative judgment (case V). Obviously, in prac-tical applications, µi − µj is not known and cannotbe used to calculate P (i > j), instead P (i > j)must be approximated. Thurstone proposed that theproportion of people who favored entity i over entityj would be an accurate approximation of P (i > j),such that:

P (i > j) =Mij

Mij +Mji(5)

where Mij is the number of people who favored entityi over entity j.

4.1.2 Elo rating systemTo produce relative measurements the comparisonsbetween subjects must be analyzed to identify anordering within the population in respect to an in-dividual trait. This was achieved using the Elo ratingsystem [21]. In essence, the Elo rating system providesa method of inferring a relative measurement fromcomparisons and is based on Thurstone’s case Vmodel [20]. Elo ratings were designed to quantify theskill of chess players. The performance of a chessplayer cannot be measured absolutely. Instead theplayer’s (relative) skill level is inferred from matchesagainst other players. This rating system solves aproblem very similar to comparative annotations. Insoft biometrics the absolute measurements of the traitscannot be directly observed due to the inaccuracy ofabsolute human descriptions. Instead we can comparetraits to infer relative measurements, similar to howchess games compare two players’ skill.

In the Elo rating system a ‘match’ is defined asa comparison between two players, A and B. Thiscomparison could be a chess game or, in the case ofsoft biometrics, a visual comparison. The outcome ofthe match is a sample of how the two players differfrom each other. The outcome is used to adjust theplayers’ ratings to reflect the sample obtained fromthe match.

R′

A = RA +K(SA − EA) (6)

R′

B = RB +K(SB − EB) (7)

The system adjusts the players’ ratings, R, based onthe result of match S. The updated rating is derivedfrom the difference between the result of a match, S(1 for a win, 0.5 for a draw and 0 for a loss), andthe expected outcome, E, given the players’ currentratings. This difference is controlled by K, which de-fines the maximum rating adjustment resulting fromthe match.

QA = 10RA/U (8)

QB = 10RB/U (9)

EA =QA

QA +QB(10)

EB =QB

QA +QB(11)

The expected outcome, E, is an adaption of equation5 based on the Bradley-Terry-Luce model [22], [23],where Q represents a player’s current rating. Theconstant U is chosen to reflect how a player’s currentrating can affect the expected result.

In chess the unknown measurement is the skill ofthe chess player - in the case of comparative annota-tions the unknown variable is the relative measure-ment of the attribute being compared. Comparisonsbetween subjects provide a measure of differencebetween the subjects’ attributes, just as chess gamescompare the skill level of the players. This informationis used to adjust the inferred relative measurementsof the two subjects.

To utilize the Elo rating system for human compar-isons a new scoring system (similar to the win-draw-loss system used in chess) is required to comparethe expected result to the actual result. Soft biometrictraits are compared using five ordered labels, theseare assigned a number ranging from -2 to 2 based ontheir order. The ‘score’ resulting from a comparisonis obtained by normalizing the given label’s value towithin 0 and 1. If the actual result reflects the expectedresult the relative measurements are not adjusted. Ifthe actual result disagrees with the expected result,the subjects’ relative measurements are adjusted inthe direction indicated by the comparison. The sizeof this adjustment is dependent on the error betweenthe actual and expected results.

In chess the maximum rating adjustment variable,K, can be kept small and over many games the skill


rating of a chess player can be slowly refined. Incontrast, our application would benefit from obtainingaccurate ratings from the least number of compar-isons. This variable can be used to ensure that relativemeasurements obtained from large numbers of com-parisons are comparable to those inferred from just afew comparisons. To perform any form of retrieval oridentification the gallery and probe biometric features(i.e. the relative measurements) must be comparableand similar. If K was a constant then the total ratingadjustment possible for N comparisons would beN ∗ K, this would mean that relative measurementsinferred from a small number of comparisons wouldnot be in the same range as those inferred from a largenumber of comparisons. To solve this K is adjustedbased on the number of comparisons available. Themaximum rating, M , is used to define K = M/Nallowing M to be fully explored by any number ofcomparisons.

The Elo rating system is used to calculate a singlecontinuous variable, representing the relative strengthof an attribute, from visual comparisons. In practiceto generate a biometric feature vector describing asuspect, we must first obtain multiple human com-parisons - comparing the suspect to multiple sub-jects (each with predefined Elo ratings). The ratingsystem begins by setting the suspect’s Elo ratings(one rating for each comparative trait) to a defaultvalue. Each comparison obtained is processed in turn,each time adjusting the suspect’s Elo ratings. Onceall the comparisons have been considered, a featurevector containing the Elo ratings is constructed. Thecontinuous values within this feature vector, calledrelative measurements, represent the relative strengthof the suspect’s physical traits and are used as abiometric signature.

The main advantage of this system is that it doesnot require exhaustive comparisons between all thesubjects to calculate an accurate relative measurement.Instead it adjusts the target’s relative measurementsbased on any available comparisons, taking into ac-count the relative measurements of the comparedsubjects. In this way the ratings for a set of playerscan be inferred from a limited set of matches betweenthem.

4.1.3 Accuracy of relative measurementsRelative measurements detail how the subject’s traitscompare to other subjects within the population. Wewould expect that the relative measurements, if ac-curate, would be strongly correlated with the actualphysical measurements of the traits. Figure 6 showsthe relationship between the relative and actual heightmeasurements. For example, the correlation betweenpixel height and relative height was statistically sig-nificant (p < 0.0001) at 0.87 - showing that the rela-tive measurements inferred from human comparisonsstrongly represent the physical traits. This implies

Fig. 6. The relationship between pixel height andrelative height

that the Elo rating system has inferred, from visualcomparisons, an accurate ordering of the subjectsbased on height.

The correlation between pixel height and the abso-lute height labels used previously (figure 2) was foundto be 0.71. This is significantly (p = 0.0018) weakerthan relative measurements mainly due to the highlysubjective and categorical nature of the labels.

The relative measurements shown in figure 6 wereinferred from all the comparisons in the human com-parison database. In application settings we wouldseek to compare against a minimum number of sub-jects to achieve an accurate relative measurement.Figure 7 shows the correlation between relative heightand pixel height for varying numbers of comparisonsper subject. It can be seen that the correlation in-creases throughout the range presented (1-52 compar-isons), clearly demonstrating that additional compar-isons improve the accuracy of the resulting relativemeasurement. The correlation was within 10% of itsterminal value after 9 comparisons. We appreciate thatthis convergence rate perhaps depends on the sizeof the database used and convergence could requirea greater number of comparisons with an increasedgallery size. Interestingly the Police and CriminalEvidence Act [24] states that an ideal identity paradeshould consist of 8 to 12 people, implying that arequirement of 9 comparisons would be suitable forapplication environments.

4.2 Verbal Identification from Soft BiometricDatabase

Biometric recognition aims to identify an unknownsubject by comparing their biometric signature to adatabase of biometric signatures. This type of identi-fication is only possible when a database of biometricdata is already available. A biometric database couldbe constructed using previous human comparisons orobtained from other forms of human representation.


Fig. 7. Correlation between pixel height and relativeheight with varying amounts of comparisons per sub-ject.

This section will focus on identifying a subject from asoft biometric database formed from previous com-parisons whilst section 4.3 will focus on automaticretrieval from video footage.

Soft biometric identification would be ideally suitedto criminal investigations where an eyewitness de-scription is available as well as a database of possiblesuspects each with soft biometric information, in thiscase obtained from previous human comparisons. Theeyewitness would compare the suspect they observedto multiple subjects from the criminal database. Basedon the given comparisons a soft biometric featurevector representing the suspect would be inferred andused to query the database. The subjects within thedatabase would be ordered based on their similarityto the feature vector. Figure 8 shows a diagram de-tailing the identification process. Querying criminaldatabases using physical descriptions is already com-mon practice within police investigations althoughcurrently it is performed using absolute labels andestimates of continuous traits rather than comparativedescriptions [25].

The identification experiment aims to retrieve a sus-pect from an 80 subject database (introduced in sec-tion 3.1). The biometric signatures within the databaseconsist of all the 19 traits (table 1), where comparativetraits are represented as relative measurements andabsolute traits represented as a vector indicating theassigned label. The process starts by selecting a targetfrom the database. n randomly sampled comparisonsbetween the target and other subjects will be removedfrom the database and used to infer the suspect’s bio-metric signature used to query the database (knownas the probe). This replicates the eyewitness compar-ing the suspect to n subjects from the database. nwill be varied to investigate how many comparisonsare required to retrieve a suspect accurately. The sus-

Fig. 8. Verbal identification from soft biometricdatabase

pect’s remaining comparisons will be used to producethe biometric signature stored within the database(known as the gallery). The similarity between theprobe and every subjects’ biometric signature withinthe database will be assessed using the Euclideandistance metric. The subjects will be ordered based ontheir similarity to the probe. The position of the sus-pect’s gallery biometric signature within the orderedlist shows the retrieval performance of the system. Thesuspect is correctly identified if their gallery signatureis positioned first in the ordered list (known as rank1). This process will be repeated until the retrievalperformance for a certain n remains constant.

The retrieval results shown in this paper are ob-tained from exhaustively calculating the similaritybetween the probe and each gallery signature. Forlarger databases this process could be accelerated byfiltering the subjects based on soft biometric featureswhich are reliably and accurately described. It shouldbe noted that in this experiment each annotator has,on average, compared 10 of the 80 subjects withinthe database. In application settings a single annotatorwould not describe such a large proportion of the sub-jects. Given the reduced subjectiveness of comparativedescriptions, the comparisons are likely to be the sameif more annotators were available and hence we donot believe this biases the experiment.

The recognition accuracy (i.e. rank 1 retrieval accu-racy) over varying numbers of probe comparisons (n)is shown in figure 9. The recognition accuracy usingjust one comparison to construct the probe is 47%.Obviously one comparison only tells us how subjectsdiffer and the resulting relative measurements arevery inaccurate. Interestingly this result matches therecognition accuracy when using categorical labels, asseen in figure 3. As more comparisons are received,the accuracy of the probe’s relative measurementsincrease, leading to improved recognition results. Itcan be seen with 9 comparisons a 91% correct recog-


Fig. 9. Recognition accuracy using relative measure-ments obtained from different numbers of comparisons

Fig. 10. Retrieval accuracy of absolute labels andrelative measurements inferred from 1 comparison and10 comparisons

nition rate is achieved. This demonstrates that ac-curate relative measurements are very distinct. Therecognition performance continues increasing over therange shown, achieving a 95% correct recognition ratewith 20 comparisons. Figure 10 shows the retrievalperformance of both relative measurements and ab-solute labels (absolute labels obtained from [1], moredetails in section 2.2). Relative measurements inferredfrom just one comparison outperform absolute labels,achieving a 90% retrieval accuracy at rank 10 (i.e. 90%chance of the suspect being in the first 10 subjectsreturned from the database) compared to rank 15.As more comparisons are obtained relative measure-ments vastly outperform absolute labels, achieving a99% retrieval accuracy at rank 5 with 10 comparisons.

Figure 11 shows an unsuccessful identification. Itcan be observed that the subjects appear very similar- both having a very similar build, hair length andskin color. The relative measurements of the subjects’traits reflect these similarities resulting in confusionbetween the two. In comparison, figure 12 showsa subject who was retrieved successfully even with

Fig. 11. Incorrect retrieval with 10 comparisons. Left:Database probe. Right: Retrieved subject

Fig. 12. Subject achieved correct retrieval with onlyone comparison

only one comparison. The male subject has long hair,which is uncommon in the Soton gait dataset, and isalso particularly tall. These traits result in a distinctset of relative measurements making retrieval verysuccessful.

As with any biometric system, the scalability ofthis technique is very difficult to ascertain. In thisexperiment we have shown that identification is ex-tremely accurate on a database of 80 subjects, wehave also shown that increasing the number of com-parisons provided also increases the identificationperformance. It would be reasonable to assume thatwith larger databases the number of comparisons col-lected would dictate the accuracy of the system. Thisprovides a mechanism by which the accuracy couldbe scaled based on the application requirements. Itis important to consider that many of the potentialapplications of such a technique are currently eithersearched manually (surveillance footage) or utilizecrude absolute labels to filter huge databases (criminaldatabases). Any retrieval system which was able toreduce the number of individuals which requiredmanual evaluation would save money and speed upthe process of searching for individuals. It is fair to as-sume that the advantages of comparative descriptionsover absolute labels are a constant and will not varywith database size. For this reason the utilization ofcomparative descriptions will provide improvementsto the filtering currently exploited within the consid-ered applications.


4.3 Verbal Identification from Video Footage

Traditional biometrics identify people by matchingbiometric signatures. This restricts identification tosituations where the subject’s biometric signature canbe obtained and only permits identification of thosesubjects whose biometric signature has previouslybeen recorded. Soft biometrics are similar, in that itidentifies people by matching signatures. The majordifference is that a biometric signature based on rel-ative measurements can be obtained from multiplesources. We have shown how relative measurementscan be inferred from human descriptions (section 4.1).Many situations may require the described subject tobe identified based on images, surveillance footage,bodily measurements or different biometric signa-tures. An exciting application of such an approachcould be to search surveillance footage around acrime scene for people matching a human descrip-tion obtained from an eyewitness. This section willintroduce how we can deduce relative measurementsfrom visual and biometric representations, focusingon gait signatures. The techniques introduced in thissection serve to demonstrate that searching video us-ing human descriptions is possible - justifying futureresearch within this area.

To reliably determine relative measurements from avisual representation of a human, the representationmust:

• be a standard signature allowing comparisonsbetween different individuals.

• contain visual features which relate to or describethe soft features we are trying to determine.

Biometric signatures are a suitable form of repre-sentation as they focus on producing visual featureswhich are consistent and comparable between dif-ferent individuals. To accurately determine relativemeasurements the human representation must con-tain information about the soft traits (shown in table1) which compose the soft biometric signature. In thiscase we are using mostly bodily features, making gaitsignatures the ideal biometric to use for surveillanceapplications. For these reasons gait signatures werestudied within this research. It is important to notethat any visual or biometric signature which encom-passed the traits being described could be used.

Average silhouette gait signatures describe the av-eraged summation of a subject’s binary silhouettesacross one gait cycle [26]. This representation encom-passes the physical features which relate to the traitspresented within table 1, which was believed to allowaccurate regression. The unwrapped silhouette gaitsignature proposed by Wang et al. was also imple-mented [27]. The advantage of this signature is thatmany of the physical measurements described withinthe soft traits are explicitly measured (in terms ofpixel distance from centroid) within the gait signaturerather than being implicit within the pixel data. A

(a) (b)

Fig. 13. Example gait signatures for the subject shownin figure 12. a) Average gait signature b) Unwrappedsilhouette gait signature

comparison of the two gait signatures can be seen infigure 13. A third gait signature, fusing both the aver-age and unwrapped gait signatures into one featurevector, was also investigated.

The current scenario shows the consistency betweenmeasures derived from human labeling and those de-rived by automated analysis of gait data. The gait datais laboratory based side view data as used in manyearly approaches to gait biometrics, we demonstratethat it is possible to derive this set of relative measuresfrom this gait data, and this set is much extended fromother approaches such as those which focus on genderalone. Application data requires translation to otherviewpoints and that represents an extension of thematerial presented here in exactly the same way thatviewpoint invariant gait biometrics was an extensionto studies previously conducted in the planar view[28].

Automated gait analysis could be performed bydirectly measuring features present within the gaitsignature. By measuring features we are assumingwe understand the precise meaning behind the label.Automated methods can be used to detect the corre-lations between visual features and their associatedlabels, especially if a model-based approach is incor-porated in the procedure.

A support vector machine (SVM) was used to pre-dict a relative measurement given a gait signature.The gait signatures were represented as a vector ofvalues representing either pixel intensities (averagegait signature) or pixel distance from centroid (un-wrapped silhouette gait signature). The relative mea-surements were normalized to a range of 0-1 andused to train the SVM. Each trait was consideredin turn and the system was validated using 10-foldcross validation. The three absolute categorical labelswithin the soft biometric signature (see table 1) werepredicted using a SVM configured for classificationrather than regression.

Figure 14 shows the correct classification rate ofthe categorical labels generated automatically fromgait signatures. Both average and unwrapped gait


Gait Signature Comparative features Absolute featuresAverage 13.9% 26.7%

Unwrapped 13.4% 26.7%Average+Unwrapped 12.8% 16.2%

TABLE 2Errors present in features obtained automatically from

different gait signatures

Fig. 14. Correct classification rate of absolute labels

signatures achieved a 73.3% average correct classifi-cation rate (CCR). It can be seen that gender is verysuccessfully classified. Gender has been shown to behighly correlated with other physical features [29](especially height) which makes it easy to determinefrom both gait signatures. Skin color and ethnicityboth rely on information which is not encoded withinthe signatures resulting in poorer classification per-formance. The Soton gait database is largely com-posed of Caucasian individuals, making up 70% of thedatabase. Given the little visual information availableto classify these traits, it is optimal to classify allsubjects as the mode class, hence the roughly 70%accuracy. The fusion signature exceeds the CCR of theother two signatures resulting in a CCR of 83.8%.

Predicting gender from gait signatures is a growingresearch area with interest in bolstering gait biomet-

Fig. 15. Errors present in relative measurements ob-tained automatically from different gait signatures

rics with additional soft traits. Obviously the aim ofour research differs from the research within this area,although it does offer a performance comparison forpredicting this particular trait. Hu et. al. [30] utilizeGabor wavelets to obtain low level features of gaitsilhouettes. Gender classification is achieved usingHidden Markov Models. A 96.77% correct classifica-tion rate was obtained when predicting gender fromsilhouettes recorded from a side on viewpoint. Yooet. al. [31] utilized SVMs trained to classify genderbased on model based gait signatures obtained fromthe Soton gait database. A 96% correct classificationrate was achieved. In this paper we have achieved a94.7% correct classification rate when predicting gen-der from a gait signature composed of both averageand unwrapped signatures.

Figure 15 shows the errors present within relativemeasurements generated automatically from differentgait signatures. The relative measurements used totrain the SVM were all normalized to the range 0-1 and as such the accuracy was the amount of er-ror between the training and the generated relativemeasurements expressed in proportion. The averageerror rates of the different gait signatures can be seenin table 2. It can be observed that the unwrappedsignature performed slightly better than the averagegait signature. Improvements of 4% (a 0.6% errorreduction) were found by fusing both the average andunwrapped gait signatures into one feature vector,exploiting the advantages of both signatures.

To determine the application potential of such asystem, we must also consider the retrieval accuracy.The retrieval process is identical to that introducedin section 4.2 although all the subjects’ soft biometricfeature vectors within the gallery are generated auto-matically from the fused average and unwrapped gaitsignatures.

Based on figures 14 and 15 it can be seen thatsome traits are more reliably predicted from gaitsignatures. When considering retrieval, these accuratetraits should be favored. Feature set selection wasused to weight the importance of each trait in theretrieval process. The feature weights were definedusing a genetic algorithm based feature set selection,and the resulting weights used to bias the euclideandistance which defines the similarity between twobiometric signatures (probe and gallery in this case).

The resulting retrieval performance can be seen infigure 16. Although the retrieval accuracy is only 20%at rank 1, it quickly increases achieving a 69% retrievalaccuracy at rank 9 and a 90% at rank 19. This re-sult highlights the possibility of automatically filteringvideo data based on a description. Improvements maybe found with the use of model-based gait signa-tures, which would provide a stronger relationshipbetween the gait signatures and soft biometric relativemeasurements. This would increase the accuracy ofthe automatically generated relative measurements


Fig. 16. Retrieval accuracy using relativemeasurements obtained automatically fromunwrapped+average gait signatures

leading to improved retrieval results. We believe theseresults represent a good start to this difficult problem.Future research will aim to improve the retrievalperformance.

5 DISCUSSION AND CONCLUSIONS

Soft biometrics allow identification based on naturalhuman descriptions of behavioral and physical traits.Identification is only possible if the description isaccurate and detailed. Conventional forms of humandescription focus on absolute labels and estimationswhich can be unreliable due to their highly subjec-tive nature and the errors common with continuousestimations. Visual comparisons composed of categor-ical labels have been introduced as an alternative -increasing the objectivenesss of labeled descriptionswhilst inferring informative and robust continuousmeasurements of the subject’s traits.

Comparisons between subjects from the Soton gaitdatabase were collected. Each annotator was askedto compare a single target to multiple subjects. Itwas found that comparative descriptions differed on17% of occasions when compared against absolutecategorical descriptions. Comparative annotations oftraits which were poorly described using absolutelabels were found to differ by up to 40%, suggestingthe comparative annotations contained new and moredetailed information.

Human comparisons must be anchored before theycan be used as a biometric signature. The Elo ratingsystem was used to infer an ordering between subjectsin respect to a specific trait. This produced a relativemeasurement of the trait in respect to the rest of thepopulation. The relative measurements were shown tostrongly represent actual trait measurements; resultscomparing actual height to the inferred relative heightshowed a correlation of 0.87.

Biometric signatures consisting of 19 relative mea-surements were exploited to allow biometric identifi-cation. Recognition of subjects from a soft biometricdatabase demonstrated the discriminative power ofrelative measurements, achieving a recognition accu-racy of 92% with ten comparisons. This increased to95% with 20 comparisons.

Relative measurements were also obtained auto-matically from video footage using a support vectormachine, allowing video data to be searched automat-ically using a human description. A retrieval accuracyof 90% was achieved at rank 19, permitting a databaseof videos to be filtered automatically. Future researchwill attempt to improve the retrieval results allowingautomatic identification from video footage.

Comparative descriptions have been shown to con-tain more discriminative information and present aninnovative approach to obtaining robust human de-scriptions for soft biometrics and possibly eyewitnessdescriptions.

REFERENCES

[1] S. Samangooei and M. S. Nixon, “Performing Content-basedRetrieval of Humans using Gait Biometrics,” Multimedia Toolsand Applications, vol. 49, no. 1, pp. 195–212, 2010.

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Daniel Reid is a PhD student at the Uni-versity of Southampton researching into hu-man identification using soft biometrics. Hisresearch interests include computer vision,statistics and machine learning. Prior to hisPhD, Daniel studied at the University ofReading achieving a Masters in Cyberneticsand Artificial Intelligence.

Mark Nixon is the Professor in ComputerVision at the University of Southampton UK.His research interests are in image process-ing and computer vision. His team developsnew techniques for static and moving shapeextraction which have found application inautomatic face and automatic gait recogni-tion and in medical image analysis. His teamwere early workers in face recognition, latercame to pioneer gait recognition and morerecently joined the pioneers of ear biomet-

rics. Amongst research contracts, he was Principal Investigator withJohn Carter on the DARPA supported project Automatic Gait Recog-nition for Human ID at a Distance and he was previously with theFP7 Scovis project and is currently with the EU-funded Tabula Rasaproject. His vision textbook, with Alberto Aguado, Feature Extractionand Image Processing (Academic Press) reached 3rd Edition in2012 and has become a standard text in computer vision. With TieniuTan and Rama Chellappa, their book Human ID based on Gait is partof the Springer Series on Biometrics and was published in 2005. Hehas chaired/ program chaired many conferences (BMVC 98, AVBPA03, IEEE Face and Gesture FG06, ICPR 04, ICB 09, IEEE BTAS2010) and given many invited talks. Mark is a member of IAPR TC4Biometrics and of the IEEE Biometrics Council. Dr. Nixon is a FellowIET and FIAPR.

Sarah Stevenage is a cognitive psychologistworking in the field of identity and identifica-tion. Following a degree and PhD in cognitivepsychology at the University of Exeter, UK,she now works as a Senior Lecturer and As-sociate Dean at the University of Southamp-ton. Her interests extend into the forensicpsychology domain, with particular interestsin novel forms of biometric identification, to-gether with the linkage between real worldand digital identity footprints.

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SOFT BIOMETRICS: HUMAN IDENTIFICATION USING … · 2020. 10. 7. · SOFT BIOMETRICS: HUMAN IDENTIFICATION USING COMPARATIVE DESCRIPTIONS 1 Soft Biometrics; Human Identiﬁcation using