Face Recognition

1
Face Recognition

• A Literature Review
• By Xiaozhen Niu
• Department of Computing Science

2
Contents

• Face Segmentation/Detection
• Facial Feature extraction
• Face Recognition
• Video-based Face Recognition
• Comparison
• Summary
• Reference

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Face Segmentation/Detection

• Before the middle 90s, the research attention
  was only focused on single-face segmentation. The
  approaches included
• Deformable feature-based template
• Neural network
• Using skin color

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Face Segmentation/Detection

• During the past ten years, considerable progress
  has been made in multi-face recognition area,
  includes
• Example-based learning approach by Sung and
  Poggio (1994).
• The neural network approach by Rowley et al.
  (1998).
• Support vector machine (SVM) by Osuna et al.
  (1997).

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Example-based learning approach (EBL)

• Three parts
• The image is divided into many possible-overlappin
  g windows, each window pattern gets classified as
  either a face or not a face based on a set of
  local image measurements.
• For each new pattern to be classified, the system
  computes a set of different measurements between
  the new pattern and the canonical face model.
• A trained classifier identifies the new pattern
  as a face or not a face.

6
Example of a system using EBL
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Neural network (NN)

• Kanade et al. first proposed an NN-based approach
  in 1996.
• Although NN have received significant attention
  in many research areas, few applications were
  successful.
• Why?

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Neural network (NN)

• Its easy to train a neural network with samples
  which contain faces, but it is much harder to
  train a neural network with samples which do not.
• The number of non-face simples are just too
  large.

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Neural network (NN)

• Neural network-based filter. A small filter
  window is used to scan through all portions of
  the image, and to detect whether a face exists in
  each window.
• Merging overlapping detections and arbitration.
  By setting a small threshold, many false
  detections can be eliminated.

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An example of using NN
11
Test results of using NN
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SVM

• SVM was first proposed in 1997, it can be viewed
  as a way to train polynomial neural network or
  radial basic function classifiers.
• Can improve the accuracy and reduce the
  computation.

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Comparison with EBL

• Test results reported in 1997.
• Using two test sets (155 faces). SVM achieved
  better detection rate and fewer false alarms.

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Recent approaches

• Face segmentation/detection area still remain
  active, for example
• An integrated SVM approach to multi-face
  detection and recognition was proposed in 2000.
• A technique of background learning was proposed
  in August 2002.
• Still lots of potential!

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Static face recognition

• Numerous face recognition methods/algorithms
  have been proposed in last 20 years, several
  representative approaches are
• Eigenface
• LDA/FDA
• Neural network (NN)

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Eigenface

• The basic steps are
• Registration. A face in an input image first must
  be located and registered in a standard-size
  frame.
• Eigenpresentation. Every face in the database can
  be represented as a vector of weights, the
  principal component analysis (PCA) is used to
  encode face images and capture face features.
• Identification. This part is done by locating the
  images in the database whose weights are the
  closest (in Euclidean distance) to the weights of
  the test images.

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LDA/FDA

• Face recognition method using LDA/FDA is called
  the fishface method.
• Eigenface use linear PCA. It is not optimal to
  discrimination for one face class from others.
• Fishface method seeks to find a linear
  transformation to maximize the between-class
  scatter and minimize the within-class scatter.
• Test results demonstrated LDA/FDA is better than
  eigenface using linear PCA (1997).

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Test results of LDA

• Test results of a subspace LDA-based face
  recognition method in 1999.

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Video-based Face Recognition

• Three challenges
• Low quality
• Small images
• Characteristics of face/human objects.
• Three advantage
• Allows Provide much more information.
• Tracking of face image.
• Provides continuity, this allows reuse of
  classification information from high-quality
  images in processing low-quality images from a
  video sequence.

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Basic steps for video-based face recognition

• Object segmentation/detection.
• Motion structure. The goal of this step is to
  estimate the 3D depths of points from the image
  sequence.
• 3D models for faces. Using a 3D model to match
  frontal views of the face.
• Non-rigid motion analysis.

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Recent approaches

• Most video-based face recognition system has
  three modules for detection, tracking and
  recognition.
• An access control system using Radial Basis
  Function (RBS) network was proposed in 1997.
• A generic approach based on posterior estimation
  using sequential Monte Carlo methods was proposed
  in 2000.
• A scheme based on streaming face recognition
  (SFR) was propose in August 2002.

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The SFR scheme

• Combine several decision rules together, such as
  Discrete Hidden Markov Models (DHMM) and
  Continuous Density HMM (CDHMM). The test result
  achieved a 99 correct recognition rate in the
  intelligent room.

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Comparison

• Two most representative and important protocols
  for face recognition evaluations
• The FERET protocol (1994).
• Consists of 14,126 images of 1199 individuals.
• Three evaluation tests had been administered in
  1994, 1996, and 1997.
• The XM2VTS protocol (1999).
• Expansion of previous M2VTS program (5 shots of
  each of 37 subjects).
• Now consists 295 subjects.
• The results of M2VTS/XM2VTS can be used in wide
  range of applications.

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1996/1997 FERET Evaluations

• Compared ten algorithms.

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Summary

• Significant achievements have been made.
  LDA-based methods and NN-based methods are very
  successful.
• FERET and XM2VTS have had a significant impact to
  the developing of face recognition algorithms.
• Challenges still exist, such as pose changing and
  illumination changing. Face recognition area will
  remain active for a long time.

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Reference

• 1 W. Zhao, R. Chellappa, A. Rosenfeld, and
  P.J. Phillips, Face Recognition A Literature
  Survey, UMD CFAR Technical Report CAR-TR-948,
  2000.
• 2 K. Sung and T. Poggio, Example-based
  Learning for View-based Human Face Detection,
  A.I. Memo 1521, MIT A.I. Laboratory, 1994.
• 3 H.A. Rowley, S. Baluja, and T. Kanade,
  Neural Network Based Face Detection, IEEE Trans.
  On Pattern Analysis and Machine Intelligence,
  Vol. 20, 1998.
• 4 E. Osuna, R. Freund, and F. Girosi, Training
  Support Vector Machines An Application to Face
  Recognition, in IEEE Conference on Computer
  Vision and Pattern Recognition, pp. 130-136,
  1997.
• 5 M. Turk and A. Pentland, Eigenfaces for
  Recognition, Journal of Cognitive Neuroscience,
  Vol.3, pp. 72-86, 1991.
• 6 W. Zhao, Robust Image Based 3D Face
  Recognition, PhD thesis, University of Maryland,
  1999.
• 7 K.S. Huang and M.M. Trivedi, Streaming Face
  Recognition using Multicamera Video Arrays, 16th
  International Conference on Pattern Recognition
  (ICPR). August 11-15, 2002.
• 8 P.J. Phillips, P. Rauss, and S. Der, FERET
  (Face Recognition Technology) Recognition
  Algorithm Development and Test Report, Technical
  Report ARL-TR 995, U.S. Army Research Laboratory.
• 9 K. Messer, J. Matas, J. Kittler, J. Luettin,
  and G. Maitre, XM2VTSDB The Extended M2VTS
  Database, in Proceedings, International
  Conference on Audio and Video-based Person
  Authentication, pp. 72-77, 1999.

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Questions