Fast features for face authentication under illumination direction changes

In this letter we propose a facial feature extraction technique which utilizes polynomial coefficients derived from 2D Discrete Cosine Transform (DCT) coefficients obtained from horizontally and vertically neighbouring blocks. Face authentication results on the VidTIMIT database suggest that the proposed feature set is superior (in terms of robustness to illumination changes and discrimination ability) to features extracted using four popular methods: Principal Component Analysis (PCA), PCA with histogram equalization pre-processing, 2D DCT and 2D Gabor wavelets; the results also suggest that histogram equalization pre-processing increases the error rate and offers no help against illumination changes. Moreover, the proposed feature set is over 80 times faster to compute than features based on Gabor wavelets. Further experiments on the Weizmann database also show that the proposed approach is more robust than 2D Gabor wavelets and 2D DCT coefficients.

[1]  David G. Stork,et al.  Pattern Classification , 1973 .

[2]  Weicheng Shen,et al.  Prolog To Face Recognition: Eigenface, Elastic Matching, And Neural Nets , 1997 .

[3]  Johan Stephen Simeon Ballot.  Face recognition using Hidden Markov Models , 2005 .

[4]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[5]  Douglas A. Reynolds,et al.  Speaker identification and verification using Gaussian mixture speaker models , 1995, Speech Commun..

[6]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[7]  H Moon,et al.  Computational and Performance Aspects of PCA-Based Face-Recognition Algorithms , 2001, Perception.

[8]  John D. Woodward,et al.  Biometrics: privacy's foe or privacy's friend? , 1997, Proc. IEEE.

[9]  Chin-Hui Lee,et al.  Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains , 1994, IEEE Trans. Speech Audio Process..

[10]  Sadaoki Furui,et al.  Recent advances in speaker recognition , 1997, Pattern Recognit. Lett..

[11]  Anastasios Tefas,et al.  Morphological elastic graph matching applied to frontal face authentication under well-controlled and real conditions , 2000, Pattern Recognit..

[12]  T. Moon.  The expectation-maximization algorithm , 1996, IEEE Signal Process. Mag..

[13]  Ah Chung Tsoi,et al.  Face recognition: a convolutional neural-network approach , 1997, IEEE Trans. Neural Networks.

[14]  Aaron E. Rosenberg,et al.  On the use of instantaneous and transitional spectral information in speaker recognition , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[15]  Chin-Chuan Han,et al.  Why recognition in a statistics-based face recognition system should be based on the pure face portion: a probabilistic decision-based proof , 2001, Pattern Recognit..

[16]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[17]  Rama Chellappa,et al.  Human and machine recognition of faces: a survey , 1995, Proc. IEEE.

[18]  Tai Sing Lee,et al.  Image Representation Using 2D Gabor Wavelets , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Ioannis Pitas,et al.  Recent advances in biometric person authentication , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[20]  Stefan Fischer,et al.  Face authentication with Gabor information on deformable graphs , 1999, IEEE Trans. Image Process..

[21]  Douglas A. Reynolds,et al.  Speaker Verification Using Adapted Gaussian Mixture Models , 2000, Digit. Signal Process..

[22]  Norman L. Johnson,et al.  Statistics and experimental design: in engineering and the physical science , 1965 .

[23]  M. A. Grudin,et al.  On internal representations in face recognition systems , 2000, Pattern Recognit..

[24]  Nalini K. Ratha,et al.  Biometric perils and patches , 2002, Pattern Recognit..

[25]  Jun Zhang,et al.  Pace recognition: eigenface, elastic matching, and neural nets , 1997, Proc. IEEE.

[26]  W. R. Buckland,et al.  Statistics and Experimental Design in Engineering and the Physical Sciences , 1978 .

[27]  Conrad Sanderson,et al.  The VidTIMIT Database , 2002 .

[28]  Y. V. Venkatesh,et al.  An integrated automatic face detection and recognition system , 2002, Pattern Recognit..

[29]  Shimon Ullman,et al.  Face Recognition: The Problem of Compensating for Changes in Illumination Direction , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Paul Wintz,et al.  Digital image processing (2nd ed.) , 1987 .

[31]  Gerhard Rigoll,et al.  Recognition of JPEG compressed face images based on statistical methods , 2000, Image Vis. Comput..

[32]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[33]  Yair Weiss,et al.  Deriving intrinsic images from image sequences , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[34]  Jean Ponce,et al.  Computer Vision: A Modern Approach , 2002 .