Photon-counting linear discriminant analysis for face recognition at a distance

Face recognition has wide applications in security and surveillance systems as well as in robot vision and machine interfaces. Conventional challenges in face recognition include pose, illumination, and expression, and face recognition at a distance involves additional challenges because long-distance images are often degraded due to poor focusing and motion blurring. This study investigates the effectiveness of applying photon-counting linear discriminant analysis (Pc-LDA) to face recognition in harsh environments. A related technique, Fisher linear discriminant analysis, has been found to be optimal, but it often suffers from the singularity problem because the number of available training images is generally much smaller than the number of pixels. Pc-LDA, on the other hand, realizes the Fisher criterion in high-dimensional space without any dimensionality reduction. Therefore, it provides more invariant solutions to image recognition under distortion and degradation. Two decision rules are employed: one is based on Euclidean distance; the other, on normalized correlation. In the experiments, the asymptotic equivalence of the photon-counting method to the Fisher method is verified with simulated data. Degraded facial images are employed to demonstrate the robustness of the photon-counting classifier in harsh environments. Four types of blurring point spread functions are applied to the test images in order to simulate long-distance acquisition. The results are compared with those of conventional Eigen face and Fisher face methods. The results indicate that Pc-LDA is better than conventional facial recognition techniques.

[1]  Lae-Jeong Park A Spatial Regularization of LDA for Face Recognition , 2010, Int. J. Fuzzy Log. Intell. Syst..

[2]  J. Walkup,et al.  Statistical optics , 1986, IEEE Journal of Quantum Electronics.

[3]  J. Friedman Regularized Discriminant Analysis , 1989 .

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

[5]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[6]  Juyang Weng,et al.  Using Discriminant Eigenfeatures for Image Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

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

[8]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[9]  John G. Proakis,et al.  Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[10]  Robert P. W. Duin,et al.  Expected classification error of the Fisher linear classifier with pseudo-inverse covariance matrix , 1998, Pattern Recognit. Lett..

[11]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[12]  Kwee-Bo Sim,et al.  Emotion Recognition Method Based on Multimodal Sensor Fusion Algorithm , 2008, Int. J. Fuzzy Log. Intell. Syst..

[13]  Aarnout Brombacher,et al.  Probability... , 2009, Qual. Reliab. Eng. Int..

[14]  Seokwon Yeom A Linear Discriminant Analysis for Low Resolution Face Recognition , 2008, 2008 Second International Conference on Future Generation Communication and Networking Symposia.

[15]  Rabia Jafri,et al.  A Survey of Face Recognition Techniques , 2009, J. Inf. Process. Syst..

[16]  Bahram Javidi,et al.  Three-dimensional distortion-tolerant object recognition using photon-counting integral imaging. , 2007, Optics express.

[17]  Tae-Yong Choi,et al.  Post-processing of Direct Teaching Trajectory in Industrial Robots , 2012, Int. J. Fuzzy Log. Intell. Syst..

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

[19]  Yong-Hyun Cho Face Recognition by Using FP-ICA Based on Secant Method , 2005, Int. J. Fuzzy Log. Intell. Syst..

[20]  Pavel Pudil,et al.  Introduction to Statistical Pattern Recognition , 2006 .