17139_Face Recognition

8/12/2019 17139_Face Recognition

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Face Reco gn i t ion

Vijay Kumar Bohat Assistant Professor (CSE)


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Facial- scans strengths

It has the ability to leverage existing imageacquisition equipment.It can search against static images such asdrivers license photographs. It is the only biometric able to operatewithout user cooperation.

...


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Facial- scans weaknesses

Changes in acquisition environment reducematching accuracy.Changes in physiological characteristicsreduce matching accuracy.It has the potential for privacy abuse due tononcooperative enrollmentand identification capabilities.


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Componen t s


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How Facia l-Scan Tech no log y

Wo r k s?Steps:Image AcquisitionImage ProcessingDistinctive Characteristics ExtractionTemplate CreationTemplate Matching


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Com pet ing Facia l-Scan

Technolog iesEigenface Feature AnalysisNeural Network

Automatic Face Processing
http://www.pages.drexel.edu/~sis26/Eigenfacehttp://www.pages.drexel.edu/~sis26/Eigenface


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Eigenfaces

Eigenface, roughly translated as ones ownface, is a technology patented at MIT thatutilizes a database of two-dimensional,grayscale facial images (Eigenfaces) fromwhich templates are created duringenrollment and verification.


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Eigenfaces.. .

These eigenvectors are derived from thecovariance matrix of the probabilitydistribution of the high-dimensional vectorspace of possible faces of human beings.


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Eigenface.. .


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Feature A n alys is


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Feature A n alys is

The most widely utilized facial recognitiontechnology.This technology is related to Eigenface, but

is more capable of accommodatingchanges in appearance or facial aspect(smiling versus frowning, for example).Visionics, a prominent facial recognitioncompany, uses Local Feature Analysis(LFA), which can be summarized as areduction of facial features to an irreducibleset of building elements.


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Featur e A n alys is ...

Feature analysis derives enrollment andverification templates from dozens offeatures from different regions of the faceand also incorporates the relative location ofthese features.The extracted features are building blocks,and boththe type of blocks and theirarrangement are used for identification andverifi-cation.


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Featur e A n alys is ...

The extracted features are building blocks,and both the type of blocks and theirarrangement are used for identification andverification. It anticipates that the slightmovement of a feature located near onesmouth will be accompanied by relativelysimilar movement of adjacent features. Since feature analysis is not a globalrepresentation of the face, it canaccommodate angles up to approximately 25

degrees in the horizontal plane, anda roximatel 15 de rees in the vertical


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Neural Netw or k

Neural network systems employ algorithmsto determine the similarity of the uniqueglobal features of live versus enrolled orreference faces, using as much of the facialimage as possible.Neural systems are designed to learn whichfeatures are most effective within the bodyof users that the system is intended toserve.


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Neur al Netw o rk ...

Features from both the enrollment and theverification faces vote on whether there is amatch.

An incorrect vote, such as a false match,prompts the matching algorithm to modifythe weight it gives to certain facial features.

Neural network systems learn which featuresare most effective for matching andpragmatically adjust themselves based onthe methods that have proven most

effective.


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Neur al Netw o rk ...

Since these technologies are capable oflearning over time, they may be capable ofreducing the time-based performanceproblems found in many facial-scansystems.

An artificial neuron is a mathematicalfunction conceived as a crude model, orabstraction of biological neurons. Artificialneurons are the constitutive units in anartificial neural network.


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A uto m at ic Face Proc ess ing

Automatic face processing (AFP) is a morerudimentary technology, using distancesand distance ratios between easily acquired

features such as eyes, end of nose, andcorners of mouth.Though overall not as robust as Eigenfaces,feature analysis, or neural network, AFP

may be more effective in dimly lit, frontalimage-capture situations.It is often used in booking stationapplications in which environmental

conditions are more controlled.


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Fac ial-Scan Deplo ym en ts

Facial-scan is generally deployed inenvironments where existing acquisitiontechnology or facial images are in place,such as public-sector ID card applications,surveillance systems, and booking stations.


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References

Samir Nanavati, Michael Thieme, RajNanavati, Biometrics: Identity Verification ina Netwrorked World, 1 st Edition, 2008 Wiley

http://visgraph.cs.ust.hk/biometrics/Papers/Multi_Modal/com-pami1993-10-01.pdf

http://doras.dcu.ie/285/1/lncs_3212.pdf http://www.securityrevue.com/article/2011/01

/close-circuit-television-cameras-survelliance-and-biometric-identification-system/

http://www.surrey.ac.uk/cvssp/research/facial anal sis/
http://visgraph.cs.ust.hk/biometrics/Papers/Multi_Modal/com-pami1993-10-01.pdfhttp://visgraph.cs.ust.hk/biometrics/Papers/Multi_Modal/com-pami1993-10-01.pdfhttp://doras.dcu.ie/285/1/lncs_3212.pdfhttp://www.securityrevue.com/article/2011/01/close-circuit-television-cameras-survelliance-and-biometric-identification-system/http://www.securityrevue.com/article/2011/01/close-circuit-television-cameras-survelliance-and-biometric-identification-system/http://www.securityrevue.com/article/2011/01/close-circuit-television-cameras-survelliance-and-biometric-identification-system/http://www.securityrevue.com/article/2011/01/close-circuit-television-cameras-survelliance-and-biometric-identification-system/http://www.surrey.ac.uk/cvssp/research/facial_analysis/http://www.surrey.ac.uk/cvssp/research/facial_analysis/http://www.surrey.ac.uk/cvssp/research/facial_analysis/http://www.surrey.ac.uk/cvssp/research/facial_analysis/http://www.surrey.ac.uk/cvssp/research/facial_analysis/http://www.securityrevue.com/article/2011/01/close-circuit-television-cameras-survelliance-and-biometric-identification-system/http://www.securityrevue.com/article/2011/01/close-circuit-television-cameras-survelliance-and-biometric-identification-system/http://www.securityrevue.com/article/2011/01/close-circuit-television-cameras-survelliance-and-biometric-identification-system/http://www.securityrevue.com/article/2011/01/close-circuit-television-cameras-survelliance-and-biometric-identification-system/http://www.securityrevue.com/article/2011/01/close-circuit-television-cameras-survelliance-and-biometric-identification-system/http://www.securityrevue.com/article/2011/01/close-circuit-television-cameras-survelliance-and-biometric-identification-system/http://www.securityrevue.com/article/2011/01/close-circuit-television-cameras-survelliance-and-biometric-identification-system/http://www.securityrevue.com/article/2011/01/close-circuit-television-cameras-survelliance-and-biometric-identification-system/http://www.securityrevue.com/article/2011/01/close-circuit-television-cameras-survelliance-and-biometric-identification-system/http://www.securityrevue.com/article/2011/01/close-circuit-television-cameras-survelliance-and-biometric-identification-system/http://www.securityrevue.com/article/2011/01/close-circuit-television-cameras-survelliance-and-biometric-identification-system/http://www.securityrevue.com/article/2011/01/close-circuit-television-cameras-survelliance-and-biometric-identification-system/http://www.securityrevue.com/article/2011/01/close-circuit-television-cameras-survelliance-and-biometric-identification-system/http://www.securityrevue.com/article/2011/01/close-circuit-television-cameras-survelliance-and-biometric-identification-system/http://www.securityrevue.com/article/2011/01/close-circuit-television-cameras-survelliance-and-biometric-identification-system/http://www.securityrevue.com/article/2011/01/close-circuit-television-cameras-survelliance-and-biometric-identification-system/http://www.securityrevue.com/article/2011/01/close-circuit-television-cameras-survelliance-and-biometric-identification-system/http://www.securityrevue.com/article/2011/01/close-circuit-television-cameras-survelliance-and-biometric-identification-system/http://www.securityrevue.com/article/2011/01/close-circuit-television-cameras-survelliance-and-biometric-identification-system/http://doras.dcu.ie/285/1/lncs_3212.pdfhttp://doras.dcu.ie/285/1/lncs_3212.pdfhttp://visgraph.cs.ust.hk/biometrics/Papers/Multi_Modal/com-pami1993-10-01.pdfhttp://visgraph.cs.ust.hk/biometrics/Papers/Multi_Modal/com-pami1993-10-01.pdfhttp://visgraph.cs.ust.hk/biometrics/Papers/Multi_Modal/com-pami1993-10-01.pdfhttp://visgraph.cs.ust.hk/biometrics/Papers/Multi_Modal/com-pami1993-10-01.pdfhttp://visgraph.cs.ust.hk/biometrics/Papers/Multi_Modal/com-pami1993-10-01.pdfhttp://visgraph.cs.ust.hk/biometrics/Papers/Multi_Modal/com-pami1993-10-01.pdfhttp://visgraph.cs.ust.hk/biometrics/Papers/Multi_Modal/com-pami1993-10-01.pdfhttp://visgraph.cs.ust.hk/biometrics/Papers/Multi_Modal/com-pami1993-10-01.pdfhttp://visgraph.cs.ust.hk/biometrics/Papers/Multi_Modal/com-pami1993-10-01.pdf


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Requ ire K n ow ledg e Set :

http://www.itl.nist.gov/div898/handbook/pmc/section5/pmc541.htm

http://en.wikipedia.org/wiki/Covariance_matrix
http://www.itl.nist.gov/div898/handbook/pmc/section5/pmc541.htmhttp://www.itl.nist.gov/div898/handbook/pmc/section5/pmc541.htmhttp://en.wikipedia.org/wiki/Covariance_matrixhttp://en.wikipedia.org/wiki/Covariance_matrixhttp://en.wikipedia.org/wiki/Covariance_matrixhttp://www.itl.nist.gov/div898/handbook/pmc/section5/pmc541.htmhttp://www.itl.nist.gov/div898/handbook/pmc/section5/pmc541.htmhttp://www.itl.nist.gov/div898/handbook/pmc/section5/pmc541.htm

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17139_Face Recognition