Face recognition using scale-adaptive directional and textural features

A novel approach to face recognition problem using directional and texture information from face images, is proposed in this paper. In order to capture the directionality, specially designed using local polynomial approximation technique, scale adaptive digital filters are used. For texture features extraction, a low dimensional and computationally effective local descriptor is utilized. Textural and directional features are captured at the holistic and part based levels resulting in a robust face descriptor. The proposed method is tested on a number of standard test face datasets (ORL, XM2VTS, Extended Yale, CMU-PIE, AR, and FERET) for different scenarios and its performance is compared with several state-of-the-art techniques.

[1]  Thomas Vetter,et al.  A morphable model for the synthesis of 3D faces , 1999, SIGGRAPH.

[2]  Ja-Chen Lin,et al.  A new LDA-based face recognition system which can solve the small sample size problem , 1998, Pattern Recognit..

[3]  Karen O. Egiazarian,et al.  Local polynomial approximation-local binary pattern (LPA-LBP) based face classification , 2011, Electronic Imaging.

[4]  Jaakko Astola,et al.  Frequency domain blind deconvolution in multiframe imaging using anisotropic spatially-adaptive denoising , 2006, 2006 14th European Signal Processing Conference.

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

[6]  Jie Chen,et al.  Fusing Local Patterns of Gabor Magnitude and Phase for Face Recognition , 2010, IEEE Transactions on Image Processing.

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

[8]  Hong Yan,et al.  Face recognition using the weighted fractal neighbor distance , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[9]  Matti Pietikäinen,et al.  Face Recognition by Exploring Information Jointly in Space, Scale and Orientation , 2011, IEEE Transactions on Image Processing.

[10]  Shuicheng Yan,et al.  Correlation Metric for Generalized Feature Extraction , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[12]  Peng Liang,et al.  Multi-resolution local binary patterns for image classification , 2010, 2010 International Conference on Wavelet Analysis and Pattern Recognition.

[13]  Zhongfei Zhang,et al.  Heat Kernel Based Local Binary Pattern for Face Representation , 2010, IEEE Signal Processing Letters.

[14]  Matti Pietikäinen,et al.  Gabor volume based local binary pattern for face representation and recognition , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[15]  Wen Gao,et al.  Histogram of Gabor Phase Patterns (HGPP): A Novel Object Representation Approach for Face Recognition , 2007, IEEE Transactions on Image Processing.

[16]  Aleix M. Martinez,et al.  The AR face database , 1998 .

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

[18]  Karen O. Egiazarian,et al.  Spatially adaptive color filter array interpolation for noiseless and noisy data , 2007, Int. J. Imaging Syst. Technol..

[19]  Jiri Matas,et al.  XM2VTSDB: The Extended M2VTS Database , 1999 .

[20]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression (PIE) database , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[21]  V. Katkovnik,et al.  Spatially adaptive color filter array interpolation for noiseless and noisy data: Articles , 2007 .

[22]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[23]  Dahua Lin,et al.  Nonparametric Discriminant Analysis for Face Recognition , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Norbert Krüger,et al.  Face recognition by elastic bunch graph matching , 1997, Proceedings of International Conference on Image Processing.

[25]  Vladimir Katkovnik,et al.  Multiresolution local polynomial regression: A new approach to pointwise spatial adaptation , 2005, Digit. Signal Process..

[26]  Yong Wang,et al.  Incremental complete LDA for face recognition , 2012, Pattern Recognit..

[27]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Alex Pentland,et al.  View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[29]  Karen O. Egiazarian,et al.  Pointwise Shape-Adaptive DCT for High-Quality Denoising and Deblocking of Grayscale and Color Images , 2007, IEEE Transactions on Image Processing.

[30]  Shihong Lao,et al.  Discriminant analysis in correlation similarity measure space , 2007, ICML '07.

[31]  Jaakko Astola,et al.  Adaptive Window Size Image De-noising Based on Intersection of Confidence Intervals (ICI) Rule , 2002, Journal of Mathematical Imaging and Vision.

[32]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[33]  Jaakko Astola,et al.  Phase Local Approximation (PhaseLa) Technique for Phase Unwrap From Noisy Data , 2008, IEEE Transactions on Image Processing.

[34]  Harry Wechsler,et al.  The FERET database and evaluation procedure for face-recognition algorithms , 1998, Image Vis. Comput..

[35]  Avinash C. Kak,et al.  PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Baochang Zhang,et al.  Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor , 2010, IEEE Transactions on Image Processing.

[37]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[38]  Jian Yang,et al.  Why can LDA be performed in PCA transformed space? , 2003, Pattern Recognit..

[39]  Andy Harter,et al.  Parameterisation of a stochastic model for human face identification , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[40]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[41]  Wen Gao,et al.  Local Gabor Binary Patterns Based on Kullback–Leibler Divergence for Partially Occluded Face Recognition , 2007, IEEE Signal Processing Letters.

[42]  Wen Gao,et al.  Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[43]  Norbert Krüger,et al.  Face Recognition by Elastic Bunch Graph Matching , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[44]  David J. Kriegman,et al.  Acquiring linear subspaces for face recognition under variable lighting , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.