Seminar: CSE 717 Soft [1] Biometric Traits in Face Recognition System

Seminar: CSE 717Soft[1] Biometric Traits in Face Recognition System

Problem Description Part

Zhi [email protected] 2/21/2004

mailto:[email protected]

04/22/23 Soft Biometric Traits in Face Recognition System - Zhi Zhang

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Ideal Characteristics of Biometric Traits

Universality Distinctiveness Permanence Collectability Performance Acceptability Circumvention[2]

Regretfully, NONE of the currently using human biometric traits possesses all of the above characteristics.


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What is Soft[1] Biometric Traits?

Traditional (Primary) Biometric Traits[2]: DNA Sequences Iris/Retina Fingerprint Voice Face Signature

The above human biometric traits are relatively universal, distinctive, permanent and resistant to circumvent. But they may not be collectable or acceptable to all the people.


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What is Soft[1] Biometric Traits? - Cont

Soft[1] Biometric Traits: Gender Ethnicity Eye/Skin/Hair color Age Height Weight

The above human biometric traits are relatively LESS distinctive, permanent and resistant to circumvent. But they provide some evidence about the user identity that could be exploited[1]


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Why using Soft Biometric Traits?

During enrollment, many existing biometric systems actually collected information like:

Gender Ethnicity Eye/Skin/Hair color Age Height Weight

If the above traits can be automatically extracted and incorporated in the decision making process, the performance of the system can be improved significantly[1].


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Necessary Devices

Image or Video DeviceAs a special Face Recognition System, an image or video device is a must for both enrollment and verification/identification.As color is a relatively important characteristic for Soft Biometric Traits, the images collected from the image or video device must be color images.


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Necessary Devices - Cont

Auxiliary Devices - OptionalFor those Soft Biometric Traits that can not be extracted directly from the images, some auxiliary devices are needed.If Height trait is expected, an extra height sensor could be installed to extract this information.If Weight trait is expected, an hidden scale could be installed to extract this information.


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Difficulty Levels of the System

Verification vs. Identification Controlled vs. Uncontrolled Database Location and Segmentation Feature Definition Feature Extraction Feature Combination Matching/Classification Decision Making


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Verification vs. Identification

Verification System 1-1 Matching Commercially available[3]

Identification System 1-n Matching Still a challenge area


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Controlled vs. Uncontrolled

Controlled Environment: Fixed pose Simple background Special/Fixed illumination

Uncontrolled Environment: Free pose Complex background Different illumination


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Database

Availability: FERET[4]

Large, 14051 images 8-bit greyscale images

Database from other universities or institutes[5]

Variable size Color images Not standard

Build our own image database


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Database - Cont

Selection of the images Demographical Distribution Gender Distribution Age Distribution Illumination Distribution - Optional Pose Distribution - Optional

Management of Database Indexing Binning


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Location and Segmentation

Behavior-based Agent Model[6]

Search the skin-like pixels by a number of color-sensitive behavior-based agents, which distributed uniformly in the 2-D image

Mark the face-like region by activating the evolutionary behavior of the agents

Examine the shape information of each face candidate region and determine the face region by fuzzy shape feature analysis

Luminance/Chrominance-Component-based Approach[7]

Detect the face location by exploring the distribution property of the luminance and chrominance components


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Feature Definition

Feature definition in Traditional (Primary) Biometric Traits

Feature definition in Soft Biometric Traits


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Feature Definition in TBT[8]

Geometric feature-based method Economical representation Insensitivity to variations in illumination and

viewpoint Sensitive to the feature extraction process

Appearance-based method Eigenfaces Karhunen-Loeve (KL) Transform or Principal

Component Analysis (PCA) Most Expressive Features (MEFs)


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Feature Definition in SBT

Gender Classification Features[9]

Feature Selection Different eigenvectors encode different kind of

information Some of the eigenvectors may be irrelevant to

gender classification Using a Genetic Algorithm (GA) to select a

subset of the eigenvectors Using the selected subset to train a Neural

Network (NN), which could be applied to perform gender classification


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Feature Definition in SBT - Cont

Ethnic Classification Features A mixture of experts consisting of ensembles

of radial basis functions for the classification of gender, ethnic origin, and pose of human faces was proposed[10]

The above work was on FERET database, which means no color information was utilized

We could acquire the skin color information after face location and segmentation process

Feature Selection combined with skin color information, which could be an important feature in ethnic classification


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Feature Definition in SBT - Cont

Age Estimation Features Relatively a new topic A classifier was designed to accept the model-

based representation of unseen images and produce an estimate of the age of the person in the image[11]

A wrinkle modeling was proposed and a research about age and gender estimation based on wrinkle texture and color of facial image was introduced[12]

We could see that both texture and color information could be applied to age estimation


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Feature Extraction

A kernel Principal Component Analysis (PCA) was proposed[13] for feature extraction

A nonlinear extension of PCA First map the input data into a feature

space via a nonlinear mapping, then apply PCA in the above feature space

Feature extraction for Soft Biometric Traits


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Feature Combination

A face verification algorithm based on multiple feature combination and supporting vector machine was proposed[15]. It combines

eigenface eigenUpper eigenTzone edge distribution

These features are projected to a new intra-person/extra-person similarity space and are evaluated by a supporting vector machine supervisor


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Matching/Classification

Various matching schemes: Neural Networks (NN) Deformable Models Hidden Markov Models (HMM) Support Vector Machines (SVM)[14]

And a lot of hybrid schemes have been applied in this field


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Decision Making

How to make a reasonable decision out of the following results: Traditional BT classification result Soft BT classification results:

gender ethnic eye/hair color age height weight


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Decision Making - Cont

Approaches could be used: Decision Tree Neural Network Bayesian approach Supporting vector machine


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System Diagram

Primary Biometric System

FeatureExtraction

ModuleMatchingModule

FaceTemplates

Soft Biometric System

FeatureExtraction

Module

Soft BiometricProcessing

Module

DecisionMakingModule


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References[1] Anil K. Jain, Sarat Dass and Karthik Nandakumar, “Soft Biometric Traits for Personal Recognition System”.[2] Anil K. Jain, Arun Ross and Salil Prabhakar, “An introduction to biometric Recognition”, IEEE Trans. on Circuits and Systems for Video Technology, Special Issue on Image- and Video-Based Biometrics, Vol. 14, No. 1, Jan. 2004.[3] P. J. Philips, P. Grother, R. J. Micheals, D. M. Blackburn, E. Tabassi, and J. M. Bone, “FRVT 2002: Overview and Summary”, March 2003, Available from: http://www.frvt.org/FRVT2002/documents.htm[4] “The Facial Recognition Technology (FERET) Database”, Available from: http://www.itl.nist.gov/iad/humanid/feret/feret_master.html[5] “Computer Vision Test Images”, Available from: http://www-2.cs.cmu.edu/~cil/v-images.html[6] Jiebo Luo, Chang Wen Chen, Parker, K.J., “Face location in wavelet-based video compression for high perceptual quality videoconferencing”, Circuits and Systems for Video Technology, IEEE Trans. on , Vol. 6 , No. 4 , Aug. 1996, pp 411 – 414.[7] Chai, D., Ngan, K.N., “Automatic Face Location for Videophone Images”, TENCON '96. Proceedings. 1996 IEEE TENCON. Digital Signal Processing Applications , Vol. 1 , 26-29 Nov. 1996, pp.137 - 140 vol.1

http://www.frvt.org/FRVT2002/documents.htm

http://www.itl.nist.gov/iad/humanid/feret/feret_master.html

http://www-2.cs.cmu.edu/~cil/v-images.html


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Reference - Cont[8] Dugelay, J.-L.; Junqua, J.-C.; Kotropoulos, C.; Kuhn, R.; Perronnin, F.; Pitas, I.; “Recent advances in biometric person authentication”, Acoustics, Speech, and Signal Processing, 2002. Proceedings. (ICASSP '02). IEEE International Conference on , Vol. 4, 13-17 May 2002, pp. IV-4060 - IV-4063 vol.4[9] Zehang Sun; Xiaojing Yuan; Bebis, G.; Louis, S.J.; “Neural-network-based Gender Classification using Genetic Search for Eigen-feature Selection”, Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on , Vol. 3 , 12-17 May 2002, pp. 2433 – 2438[10] Gutta, S.; Huang, J.R.J.; Jonathon, P.; Wechsler, H.; “Mixture of experts for classification of gender, ethnic origin, and pose of human faces”, Neural Networks, IEEE Trans. on , Vol. 11 , Issue: 4 , July 2000, pp. 948 – 960[11] Lanitis, A.; Draganova, C.; Christodoulou, C.; “Comparing Different Classifiers for Automatic Age Estimation”, Systems, Man and Cybernetics, Part B, IEEE Trans. on , Vol. 34 , Issue: 1 , Feb. 2004, pp. 621 – 628[12] Hayashi, J.; Yasumoto, M.; Ito, H.; Koshimizu, H.; “Age and Gender Estimation based on Wrinkle Texture and Color of Facial Images”, Pattern Recognition, 2002. Proceedings. 16th International Conference on , Vol. 1 , 11-15 Aug. 2002, pp. 405 - 408 vol.1[13] Kwang In Kim; Keechul Jung; Hang Joon Kim; “Face recognition using kernel principal component analysis”, Signal Processing Letters, IEEE , Vol. 9 , Issue: 2 , Feb. 2002, pp. 40 – 42


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Reference - Cont

[14] G. D. Guo, S. Z. Li, and K. L. Chan, “Face recognition by Support Vector Machines”, in Proc. Int. Conf. Automatic Face and Gesture Recognition, 2000, pp. 196-201.[15] Do-Hyung Kim; Jae-Yeon Lee; Jung Soh; Yun-Koo Chung; “Real-time face verification using multiple feature combination and a support vector machine supervisor”, Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on , Vol. 2 , 6-10 April 2003, pp. II - 353-6 vol.2

Seminar: CSE 717 Soft [1] Biometric Traits in Face Recognition System

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