Evaluating Feature Extractors and Dimension Reduction Methods for Near Infrared Face Recognition Systems

This study evaluates the performance of global and local feature extractors as well as dimension reduction methods in NIR domain. Zernike moments (ZMs), Independent Component Analysis (ICA), Radon Transform + Discrete Cosine Transform (RDCT), Radon Transform + Discrete Wavelet Transform (RDWT) are employed as global feature extractors and Local Binary Pattern (LBP), Gabor Wavelets (GW), Discrete Wavelet Transform (DWT) and Undecimated Discrete Wavelet Transform (UDWT) are used as local feature extractors. For evaluation of dimension reduction methods Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), Linear Discriminant Analysis + Principal Component Analysis (Fisherface), Kernel Fisher Discriminant Analysis (KFD) and Spectral Regression Discriminant Analysis (SRDA) are used. Experiments conducted on CASIA NIR database and PolyU-NIRFD database indicate that ZMs as a global feature extractor, UDWT as a local feature extractor and SRDA as a dimension reduction method have superior overall performance compared to some other methods in the presence of facial expressions, eyeglasses, head rotation, image noise and misalignments.

[1]  LinLin Shen,et al.  Directional binary code with application to PolyU near-infrared face database , 2010, Pattern Recognit. Lett..

[2]  Yufeng Zheng,et al.  Near infrared face recognition using orientation-based face patterns , 2012, 2012 BIOSIG - Proceedings of the International Conference of Biometrics Special Interest Group (BIOSIG).

[3]  Yun Zhang,et al.  Face recognition based on wavelet transform and SVM , 2005, 2005 IEEE International Conference on Information Acquisition.

[4]  Witold Pedrycz,et al.  Face recognition using decimated redundant discrete wavelet transforms , 2011, Machine Vision and Applications.

[5]  Xavier Maldague,et al.  Infrared face recognition: A comprehensive review of methodologies and databases , 2014, Pattern Recognit..

[6]  Chengjun Liu,et al.  Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition , 2002, IEEE Trans. Image Process..

[7]  Dattatray V. Jadhav,et al.  Radon and discrete cosine transforms based feature extraction and dimensionality reduction approach for face recognition , 2008, Signal Process..

[8]  Yuqing He,et al.  Near infrared face recognition based on wavelet transform and 2DPCA , 2010, 2010 International Conference on Intelligent Computing and Integrated Systems.

[9]  Dattatray V. Jadhav,et al.  Feature extraction using Radon and wavelet transforms with application to face recognition , 2009, Neurocomputing.

[10]  Jiawei Han,et al.  SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis , 2008, IEEE Transactions on Knowledge and Data Engineering.

[11]  Siti Mariyam Shamsuddin,et al.  Near Infrared Face Recognition: A Comparison of Moment-Based Approaches , 2014 .

[12]  Jian Yang,et al.  KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Siti Mariyam Shamsuddin,et al.  Assessment of time-lapse in visible and thermal face recognition , 2012 .

[14]  Jan Flusser,et al.  Rotation and noise invariant near-infrared face recognition by means of Zernike moments and spectral regression discriminant analysis , 2013, J. Electronic Imaging.

[15]  David Zhang,et al.  Using the idea of the sparse representation to perform coarse-to-fine face recognition , 2013, Inf. Sci..

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

[17]  M. Grgic,et al.  Appearance-based statistical methods for face recognition , 2005, 47th International Symposium ELMAR, 2005..

[18]  Jan Flusser,et al.  Near infrared face recognition by combining Zernike moments and undecimated discrete wavelet transform , 2014, Digit. Signal Process..

[19]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Dattatray V. Jadhav,et al.  Rotation, illumination invariant polynomial kernel Fisher discriminant analysis using Radon and discrete cosine transforms based features for face recognition , 2010, Pattern Recognit. Lett..

[21]  Shengcai Liao,et al.  Illumination Invariant Face Recognition Using Near-Infrared Images , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Shimon Ullman,et al.  Face Recognition: The Problem of Compensating for Changes in Illumination Direction , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Shengcai Liao,et al.  Nighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching , 2012, ACCV.

[24]  Hamid R. Rabiee,et al.  Object Tracking in Crowded Video Scenes Based on the Undecimated Wavelet Features and Texture Analysis , 2008, EURASIP J. Adv. Signal Process..

[25]  Zahir M. Hussain,et al.  Higher order orthogonal moments for invariant facial expression recognition , 2010, Digit. Signal Process..

[26]  Andrea Salgian,et al.  Face recognition with visible and thermal infrared imagery , 2003, Comput. Vis. Image Underst..

[27]  Konstantinos N. Plataniotis,et al.  Face recognition using kernel direct discriminant analysis algorithms , 2003, IEEE Trans. Neural Networks.

[28]  Hans Jørgen Andersen,et al.  Physics-based modelling of human skin colour under mixed illuminants , 2001, Robotics Auton. Syst..

[29]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..