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

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Seminar CSE 717Soft1 Biometric Traits in Face
Recognition System

• Problem Description Part
• Zhi Zhang
• zhizhang_at_cse.buffalo.edu 2/21/2004

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Ideal Characteristics of Biometric Traits

• Universality
• Distinctiveness
• Permanence
• Collectability
• Performance
• Acceptability
• Circumvention2
• Regretfully, NONE of the currently using human
  biometric traits possesses all of the above
  characteristics.

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What is Soft1 Biometric Traits?

• Traditional (Primary) Biometric Traits2
• 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 Soft1 Biometric Traits? - Cont

• Soft1 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 exploited1

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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 significantly1.

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Necessary Devices

• Image or Video Device
• As 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 - Optional
• For 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 available3
• 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
• FERET4
• Large, 14051 images
• 8-bit greyscale images
• Database from other universities or institutes5
• 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 Model6
• 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 Approach7
• 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 TBT8

• 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 Features9
• 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 proposed10
• 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 image11
• A wrinkle modeling was proposed and a research
  about age and gender estimation based on wrinkle
  texture and color of facial image was
  introduced12
• 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
  proposed13 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 proposed15. 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
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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/docum
  ents.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

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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