A frequency domain face recognition technique based on correlation plane features as input to a regression neural network

Abstract In the area of Face Recognition (FR), the frequency domain approach is a widely used technique. The value of peak-to-sidelobe ratio (PSR) of the correlation peak at the output plane is often used as a measure of recognition. However, the use of such a hard threshold can make a FR system erroneous for recognition and classification. As an alternative to the conventional hard thresholding, three different feature vectors of the correlation plane, e.g. the peak intensity, total energy and PSR, are calculated. These three feature vectors are used to train a Generalized Regression Neural Network (GRNN). The improved False Acceptance Rate (FAR) and consistent Recognition Rate (RR) is reported when tested on the Yale face database.

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

[2]  B. V. K. Vijaya Kumar,et al.  Correlation Pattern Recognition , 2002 .

[3]  Pradeep K. Khosla,et al.  Real-time face verification system on a cell-phone using advanced correlation filters , 2005, Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05).

[4]  Jen-Tzung Chien,et al.  Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  B. V. K. Vijaya Kumar,et al.  Quad Phase Minimum Average Correlation Energy Filters for Reduced Memory Illumination Tolerant Face Authentication , 2003, AVBPA.

[6]  Marios Savvides,et al.  Correlation Pattern Recognition for Face Recognition , 2006, Proceedings of the IEEE.

[7]  Marios Savvides,et al.  Performance Evaluation of Face Recognition using Visual and Thermal Imagery with Advanced Correlation Filters , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[8]  B. V. K. Vijaya Kumar,et al.  Face Recognition with Kernel Correlation Filters on a Large Scale Database , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[9]  B. V. K. Vijaya Kumar,et al.  Incremental updating of advanced correlation filters for biometric authentication systems , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[10]  D. Casasent,et al.  Minimum average correlation energy filters. , 1987, Applied optics.

[11]  Hua Yu,et al.  A direct LDA algorithm for high-dimensional data - with application to face recognition , 2001, Pattern Recognit..

[12]  Marios Savvides,et al.  Robust shift-invariant biometric identification from partial face images , 2004, SPIE Defense + Commercial Sensing.

[13]  B. V. Vijaya Kumar,et al.  Minimum-variance synthetic discriminant functions , 1986 .

[14]  B. V. K. Vijaya Kumar,et al.  Illumination Normalization Using Logarithm Transforms for Face Authentication , 2003, AVBPA.

[15]  P Refregier.  Optimal trade-off filters for noise robustness, sharpness of the correlation peak, and Horner efficiency. , 1991, Optics letters.

[16]  S. Park,et al.  Partial & Holistic Face Recognition on FRGC-II data using Support Vector Machine , 2006, CVPR Workshops.

[17]  B. V. K. Vijaya Kumar,et al.  Class Dependent Kernel Discrete Cosine Transform Features for Enhanced Holistic Face Recognition in FRGC-II , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[18]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.