Literature Assessment for Pose-Invariant Face Recognition

International Journal of Scientific Research Engineering & Technology (IJSRET)Volume 2 Issue 10 pp 653-658 January 2014 www.ijsret.org ISSN 2278 0882

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Literature Assessment for Pose-Invariant Face RecognitionS.Jebha, P.KANNAN

PG Scholar Department of ECE, PET Engineering College, IndiaProfessor Department of ECE, PET Engineering College, India

Abstract:Face recognition becomes problematic when thevariation is considered across different poses. Toovercome this problem, invariant pose is considered.For this invariant pose, Optimization algorithm is used.Using this algorithm, frontal image is reconstructedvirtually from the profile of non-frontal image. Thisliterature survey gives in detail about the differentkinds of methods which are related with variant poses.

Keywords: Invariant poses, Optimization algorithm,Fisherfaces, Appearance model, Morphable model,Light fields, Probabilistic stack-flow.

I. INTRODUCTION

Face recognition is used in many applicationssuch as access control, identification systems,surveillance, biometrics and pervasive computing. Inface recognition, pose variations is considered as oneof the important problem, which would not producebetter accuracy and performance. Face recognitionacross poses is carried out in 2D and 3D models. Manyalgorithms are implemented to compute 2D and 3Dmodels to obtain better results. The performancedegrades when there are variations in the face withpose, illumination and expression [2]. Face recognitionalgorithms such as Eigenfaces andFisherfaces[1] areused under pose variations which produce lowrecognition rates only. This algorithm cannot beapplied to 3D faces. Face recognition differ for both2D and 3D techniques.The variations across pose andilluminations can be handled by the algorithm whichhelps to simulate the process of image formation in 3Dspace, using computer graphics, and it estimates 3Dshape. This isachieved by usingmorphable model of3D faces [4].The stereo matching [22] technique in 2Dimage is to judge the similarity of two images that areviewed from different poses. This stereo matchinghelps for the purpose of arbitrary, physically valid andcontinuous correspondences. GaborFisher Classifier(GFC)method [18] is robustto illumination andvariability in facial expression. This method is appliedto feature which can be extracted from thegaborfilterthrough Enhanced Fisher linear discriminantModel (EFM).The patch transform [12] represents animage intooverlapping patches that are sampled on a

regular grid. This transform helps to influence imagesin the patch domain, which then begins the inversepatch transform to synthesize modified images.In tiedfactor analysis [19], the linear transformation dependson the pose, but theloadings are constant (tied) for agiven image. So Expectation-Maximization (EM)algorithm is usedfor the estimationof lineartransformations and noiseparameters from trainingdata.

II. LITERATURE REVIEW

A. Eigenfaces and Fisherfaces :On seeing that correlation methods arecomputationally expensive and require enormousamount of storage, it is normal to go behinddimensionality reduction schemes. A technique that iscommonly used for dimensionality reduction incomputer vision mostly in face recognition is principalcomponents analysis (PCA) [16].This technique selectsa linear projection and maximizes thescatter of allprojected samples by dimensionality reducing it.Iftheimage is linearly projected with eigenvectors ofsame dimension as in theoriginal images, then it isknown asEigenfaces.Here, the classification isperformed by means of a nearest neighbour classifierin the reduced feature space and then the Eigenfacemethod is equivalent to the correlation method. Adrawback of this method is that there is unwantedinformation in the scatter which is maximizednot onlyto the between-class scatter that is helpful forclassification, but also for the within-class scatter forclassification purposes.Thus if PCA is applied to faceimages under varyingillumination, the projectionmatrix will contain Eigenface which maintains theprojectedfeature space. The points that are present inthe projected space will not beclustered and not asgood as the classes that are grimed together.

TheEigenfacemethodhas the advantage that, thevariation within classes lies in a linear subspace of theimage space. Consequently, the classes are convex andlinearly separable. Dimensionality reduction can bedone using linear projection and canconserve linearseparability. But dimensionality reduction is still aproblem in recognizing a face as it is insensitivity tolighting conditions. Here the learning set is labelled, it


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so that theinformation can be usedto reducethedimension of the feature in an image. So, class specificlinear methods can be used fordimensionalityreduction and simple classifiers toreduce thefeature space, as a result better recognitionrates can be obtained thanthe Eigenfacemethod.Fishers Linear Discriminant (FLD) [1] is used as aclass specific method, it can shape the scatter in orderto make it additionalconsistent for classification. Theproblem in face recognition is that it is difficult tomaintain the within-class scatter matrix at all timessingular.The scatter matrix is given below,

ST= ( )( ) (1)Where Xk is the N number of sample images and isthe mean images of samples.It is feasible to decide thewithin-class scatter matrixof the projected samples tocompose it exactly to zero.In order to overcome thedifficulty of a singular scatter matrix Fisherfacemethod is used. It overcomes the problem byprojectingthe image set to a lower dimensional space and obtainthe ensuing within-class scatter matrixasnonsingular.This is attained by using PCA to reducethe dimensionof the feature space and thenFisherfacemethod is applied to reduce the dimension into non-singular matrix.

Fig 1. Comparison in the Recognition rates ofEigenface(PCA) over Fisherface.

From the above Fig 1, Fisherfacemethodhaslow errorrates than theEigenface method and less computationaltime. The disadvantage of Fisherface method is that itproduces very low recognition rates.

B. Appearance model:

Timothy F. Cootes, Gareth J. Edwards, andChristopher J. Taylorhavedescribed a method formatching the deformable models for shape andappearance oforiginal images.The advance of Edwardset al. [5], the statistical appearance models wasgenerated from the combination of shape model and itsvariation with texture model. The term texturemeansthe intensities in the pattern or thecolorstransverselyin the patch of an image. This modelis assembled fromthe training set of annotated imagesin which analogous points are marked. Forconstructing a face model, the points are marked on theface image which defines the main features.ThenProcrustes analysis is applied for the alignment ofthe sets of points in face which is represented asvector , thus a statistical shape model is built. Andthenwarp of the training image is matched with theimage so that a shape-free patch is obtained.Theappearance model has parameter ,which controls theshapeand texture in the model framefrom the givenequation below, = += + (2)Where, is the mean shape, is the mean texture in amean shapedpatch, is the texture vector and Qs, Qg arethe matrices describing the modes of variationderivedfrom the training set.To make a texture modelEigen-

Analysis applied to the image frame and is generatedby applying scaling and offset to the intensities.Then,correlations are made between shape and texture and acombined appearance model is generated. Thereconstruction can be obtained by producing thetexture in a mean shaped patch, then warping itconsequentlyto facilitate the model points lay on theimage points.The parameters of appearance model andshape transformation gives the shape of the imagepatch which aredefined by the position of the modelpoints in the image frame so as to represent the model.During matching, the pixels of the imagearesampledand projected into the texture model frame.The elements present in a row of the matrix give theweights to each texture sample point for the modelparameter.This method makes use of correlationbetween errors in model parameters and the remainingtexture errors. The disadvantage of this method is thatit cannot beused for tree like structures with varyingnumbers of branches, butit can be used forassortedshapes which exhibit shape variation butnot achange in topology. AAM search is slightly slowerthan Active Shape Model.

C. Morphable model:

Morphable model of 3D faces captures the class-specific properties of faces. These properties arelearned automatically from a data set of 3D scans. The

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morphable model represents shapes and textures offaces as vectors in a high-dimensional face space, andinvolves a probability density function of natural faceswithin face space.The algorithm used here estimates all3D scene parameters automatically, including headposition and orientation, focal length of the camera,and illumination direction. This is achieved by a newinitialization procedure that also increases robustnessand reliability of the system considerably.The newinitialization uses the image coordinates of betweensixand eight feature points.Figure.2

In morphable face model (Fig. 2), the recognition isbased on fitting the intrinsic shape and texture of faces.This model is independent of imaging conditions. Inthe fitting algorithm, all the gallery images areanalyzed for the purpose of identification. In the probeimagealso fitting algorithm is computed to obtain themodel coefficients. They are thencompared with all themodel coefficients obtained from gallery data to locatethe nearestneighbour. The morphable face model [4] isbased on a vector space representation of faces that isconstructed such that any bowed combinationof shapeand texture vectors.Continuous changes in the modelparameters generate a smooth transition such that eachpoint of the initial surface moves toward a point on thefinal surface.Dense point-to-point correspondence iscrucialfor defining shape and texture vectors. 3Dmorphable model isprevailing and adaptablerepresentation for human faces.But it requires manymanually selected landmarks for initialization.

D. Eigen light-field (ELF) method:

The importantattribute of appearance-based algorithmsis based on the recognition decision which is obtainedby considering the features as the pixel intensity valuesin an image. The pixel intensities which are consideredas features in appearance-based algorithms matchupstraight to the radiance oflight emitted from theobject along certain rays in space. The light-fieldspecifies the radiance of light along all rays in thescene. Hence, the light field of an object is the set ofall possible features that could beused by anappearance-based algorithm. Any number of imagescan be used, from one upwards, in both the gallery andthe probe sets. The light-field correctly rerendersimages across pose.This algorithm can use any numberof gallery images captured at arbitrary pose and anynumber of probe images also captured with arbitraryposes.

Fig 3. The average recognition rate is figured acrossgallery and probe poses using Eigen Light-Fields

(ELF),FaceItand Eigenfaces.

This algorithm operates by estimating the light-field ofthe subjects head. First, generic training data is usedto compute an eigen-space of head light-fields, similarto the construction of Eigen-faces. The projection intothe eigen-space is performed by setting up a least-squares problem and solving for the projectioncoefficients. This linear algorithm can be applied toany number of images, captured from any poses.Finally, matching is performed by comparing the probeand gallery light-fields using a nearest neighbouralgorithm. Capturing the complete light-field of anobject is a difficult task, primarily because it requires ahuge number of images. From Fig 3,it is inferred thatthe average recognition rate by ELF methodproducesbetter recognition rate[6] than other method such asEigenfaces and FaceIt. The Eigen Light-FieldEstimation Algorithm can be used to estimate a light-field from a collection of images. Once the light-fieldhas been estimated, it can then, theoretically at least,be used to render new images of the same object underdifferent poses. To use it for face recognition acrosspose, it needs to perform vectorization, classification,selecting training and testing sets. Vectorizationconverts measurementsof light into features for apattern recognition algorithm. The Eigen Light-FieldsAlgorithm can be extended to recognize faces acrosspose and illumination simultaneously by generalizingeigen light-fields to Fisher light-fields.

E. Probabilistic stack-flow:

Stack flow ascertains the viewpoint induced spatialdeformities undergone by a face at the patch level. Ithelps tolearn the correspondence between patchescoming from two different viewpoints of a face.In thisstack flow,a warp is consistent across the entire stackand aligns a patch in the stack of profile faces to apatch in the stack of frontal faces. It achievesresistance to noise by using the stack of images as aconstraint and avoids the averaging process. By

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avoiding the averaging, it is better to handle at texturevariation. The discriminative power of eachwarpedpatch as a function of viewpoint, which isknown as Probabilistic stack-flow [8].The probabilitydistribution for patch SSD is given as,, , { , } (3)where is the SSD score for the r-th patch, is theprobe viewpoint and the variable refers to classeswhengallery and probe images belong to the samesubject of client or different subjects ofimpostor .Here, the sum of squared differences (SSD)is used as a similarity value between a gallery patchand a warped probe patch.The limitation with thismethod is that it is inability to handle situations wherepatches become occluded at extreme viewpoints.

F. Optimization algorithm:

Even though there are many methods for facerecognition across poses, they all have low recognitionrates and attain less efficiency. In order to overcomethese problems, optimization algorithm [21] is used.This approach consists of two methods. The first one isthe Markov Random Fields (MRF) and second one isthe Belief Propagation (BP) algorithm. This approachcan produce high recognition rates and increase inefficiency.

Fig 4.Recognition rates ofdifferent multi-pie databaseat various viewpoints.

From the above Fig 4, it is inferred that for variousmethods the obtained recognition rates fluctuates. Themethod MRF and BP gives better recognition rate thanother methods.

a) Markov Random Fields:

Markov random field models provide a robust andintegrated framework for early vision problems such as

stereo, optical flow and image restoration. MRF helpsto find the globally optimal set of local warps that canbe used to predict the image patches at the frontalview. In order to reduce the effect of illuminationchanges, the local patches are normalized bysubtracting the means and dividing by the standarddeviations before estimating the sum of squareddifference in the overlapping region. The optimal setof warps is found by minimizing the energy function.After minimizing, the energy function is given as,{ }= ( ) + , (4)( , )Where pi and pj are the warps taken from two differentnodes.

b) Belief Propagation:

Priority-BP can, thus, be viewed as a generic way forefficiently applying belief-propagation to MRFs withvery large discrete state spaces, thus dealing, for thefirst time, with what was considered as one of the mainlimitations of BP up to now. At this point, it should benoted that priority-BP, as any other version of BP, isonly an approximate global optimizer. Priority-beliefpropagation (BP) algorithm, which carries 2 majorimprovements over standard BP[10]: dynamiclabelpruning and priority-based message scheduling.Priority-BP is a generic algorithm, applicable to anyMRF energy function. BP is an iterative algorithm thattries to find a MAP estimate by iteratively solving afinite set of equations until a fixed point is obtained.BP continuously propagates local messages betweenthe nodes of an MRF graph. At every iteration, eachnode sends messages to all of its neighbouring nodes,while it also accepts messages from these nodes.During the forward pass vigorously reduces thenumber of possible labels for each node by discardinglabels that are unlikely to be assigned to thatnode.After all MRF nodes have pruned their labels atleast once then it can precomputed by reducing thematrices of pair wise potentials and thus the speed ofBP algorithm is increased. The use of messageschedulingto determine the transmitting order for anode based onthe confidence of that node about itslabels. The node most confident about its label shouldbe the first one to transmit outgoing messages to itsneighbours.

III. CONCLUSION

In this paper, literature assessment for pose-invariantface recognition is discussed in concise. It is seen that

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the optimization algorithm provides fine recognitionrates than any other recognition methods.

ACKNOWLEDGEMENT

Apart from the efforts of me, the success of any workdepends largely on the encouragement and guidelinesof many others. I take this occasion to express mygratefulness to the people who have been influential inthe successful completion of this work.I would like toextend my sincere thanks to all of them. I owe asincere prayer to the LORD ALMIGHTY for his kindblessings and giving me full support to do this work,without which this would have not been possible. Iwish to take this opportunity to express my gratitude toall, who helped me directly or indirectly to completethis paper.

REFERENCES

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IEEE Trans. Image Process., vol. 16, no. 7, pp. 17161725, Jul. 2007.[10] N. Komodakis and G. Tziritas, Image completionusing efficient belief propagation via priorityscheduling and dynamic pruning, IEEE Trans.ImageProcess., vol. 16, no. 11, pp. 26492661, Nov. 2007.[11] J. Yedidia, W. Freeman, and Y. Weiss,Understanding belief propagation and itsgeneralizations, Expl. Artif.Intell. New Mill., vol. 2,pp. 239269, Nov. 2003.[12] T. Cho, S. Avidan, and W. Freeman, The patchtransform, IEEE Trans. Pattern Anal. Mach. Intell.,vol. 32, no. 8, pp. 14891501, Aug. 2010.[13] V. Blanz and T. Vetter, A morphable model forthe synthesis of 3D faces, in Proc. SIGGRAPH Conf.,1999, pp. 187194.[14] W. Zhang, S. Shan, W. Gao, X. Chen, and H.Zhang, Local gabor binary pattern histogramsequence (LGBPHS): A novel non statistical modelFRO face representation and recognition, in Proc. Int.Conf. Comput.Vis., 2005, pp. 786791.[15] H. Gao, H. Ekenel, and R. Stiefelhagen, Posenormalization for local appearance-based facerecognition, in Proc. Int. Conf. Adv. Biometr., 2009.[16] T. Kanade and A. Yamada, Multisubregionbased probabilistic approach toward pose-invariantface recognition, in Proc. Symp. Comput. Int.Robot.Autom., Jul. 2005, pp. 954959.[17] S. Biswas and R. Chellappa, Pose-robust albedoestimation from asingle image, in Proc. Comput. Vis.Pattern Recognit., Jun. 2010,pp. 26832690.[18] C. Liu and H. Wechsler, Gabor feature basedclassification using the enhanced fisher lineardiscriminant model for face recognition, IEEETrans.Image Process., vol. 11, no. 4, pp. 467476, Apr. 2002.[19] S. Prince, J. Elder, J. Warrell, and F. Felisberti,Tied factor analysis for face recognition across largepose differences, IEEE Trans. PatternAnal. Mach.Intell., vol. 30, no. 6, pp. 970984, Jun. 2008.[20] R.A. Fisher, The Use of Multiple Measures inTaxonomic Problems, Ann. Eugenics, vol. 7, pp. 179-188, 1936[21] HuyThoHoand Rama Chellappa, Pose-InvariantFace Recognition Using Markov RandomFields,IEEE Trans. Image Process., vol. 22, no. 4,Apr.2013.[22] C. Castillo and D. Jacobs, Using stereomatching with general epopolar geometry for 2D facerecognition across pose, IEEE Trans. PatternAnal.Mach. Intell., vol. 31, no. 12, pp. 22982304, Dec.2009.


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Fig 2.Morphable face model performed by the comparison of model coefficients.

Database of3D scans

Morphableface model

FittingGalleryimages

Probeimage

Fitting Modelcoefficients

Modelcoefficients

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Literature Assessment for Pose-Invariant Face Recognition