Face Recognition A Literature Review By Xiaozhen Niu Department of Computing Science.

Face RecognitionA 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

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/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)

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”.

Example of a system using EBL

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?

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 (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.

An example of using NN

Test results of using NN

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.

Comparison with EBL

Test results reported in 1997. Using two test sets (155 faces).

SVM achieved better detection rate and fewer false alarms.

Recent approachesFace 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

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

Eigenface LDA/FDA Neural network (NN)

EigenfaceThe 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 a subspace LDA-based

face recognition method in 1999.

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

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 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 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.

ComparisonTwo 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 Compared ten algorithms.

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.

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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