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Face Recognition. A Literature Review By Xiaozhen Niu Department of Computing Science. Contents. Face Segmentation/Detection Facial Feature extraction Face Recognition Video-based Face Recognition Comparison Summary Reference. Face Segmentation/Detection.

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face recognition

Face Recognition

A Literature Review

By Xiaozhen Niu

Department of Computing Science

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

Before the middle 90’s, the research attention was only focused on single-face segmentation. The approaches included:

  • Deformable feature-based template
  • Neural network
  • Using skin color
face segmentation detection4
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).
example based learning approach ebl
Example-based learning approach (EBL)

Three parts:

  • The image is divided into many possible-overlapping 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”.
neural network nn
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.


neural network nn8
Neural network (NN)
  • It’s 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.
neural network nn9
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.
  • 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.
comparison with ebl
Comparison with EBL
  • Test results reported in 1997.
  • Using two test sets (155 faces). SVM achieved better detection rate and fewer false alarms.
recent approaches
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!

static face recognition
Static face recognition

Numerous face recognition methods/algorithms have been proposed in last 20 years, several representative approaches are:

  • Eigenface
  • Neural network (NN)

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.
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).
test results of lda
Test results of LDA
  • Test results of a subspace LDA-based face recognition method in 1999.
video based face recognition
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.
basic steps for video based face recognition
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.
recent approaches21
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.
the sfr scheme
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.

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.
1996 1997 feret evaluations
1996/1997 FERET Evaluations
  • Compared ten algorithms.
  • 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.

[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.