Expressing Facial Structure and Appearance Information in Frequency Domain for Face Recognition

Beneath the uncertain primitive visual features of face images are the primitive intrinsic structural patterns (PISP) essential for characterizing a sample face discriminative attributes. It is on this basis that this paper presents a simple yet effective facial descriptor formed from derivatives of Gaussian and Gabor Wavelets. The new descriptor is coined local edge gradient Gabor magnitude (LEGGM) pattern. LEGGM first uncovers the PISP locked in every pixel through determining the pixel gradient in relation to its neighbors using the Derivatives of Gaussians. Then, the resulting output is embedded into the global appearance of the face which are further processed using Gabor wavelets in order to express its frequency characteristics. Additionally, we adopted various subspace models for dimensionality reduction in order to ascertain the best fit model for reporting a more effective representation of the LEGGM patterns. The proposed descriptor-based face recognition method is evaluated on three databases: Plastic surgery, LFW, and GT face databases. Through experiments, using a base classifier, the efficacy of the proposed method is demonstrated, especially in the case of plastic surgery database. The heterogeneous database, which we created to typify real-world scenario, show that the proposed method is to an extent insensitive to image formation factors with impressive recognition performances.

[1]  Nicolas Pinto,et al.  How far can you get with a modern face recognition test set using only simple features? , 2009, CVPR.

[2]  Wen Gao,et al.  Interpolation-Dependent Image Downsampling , 2011, IEEE Transactions on Image Processing.

[3]  Massimo Tistarelli,et al.  Feature Level Fusion of Face and Fingerprint Biometrics , 2007, 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems.

[4]  Xin Liu,et al.  Face Recognition after Plastic Surgery: A Comprehensive Study , 2012, ACCV.

[5]  Susan J. Lederman,et al.  Haptic Face Processing and Its Relation to Vision , 2010 .

[6]  Himanshu S. Bhatt,et al.  Recognizing Surgically Altered Face Images Using Multiobjective Evolutionary Algorithm , 2013, IEEE Transactions on Information Forensics and Security.

[7]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[8]  Aneesh Krishna,et al.  Face recognition using various scales of discriminant color space transform , 2012, Neurocomputing.

[9]  Yuxiao Hu,et al.  Learning a locality preserving subspace for visual recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[10]  Terrance E. Boult,et al.  GRAB: generalized region assigned to binary , 2013, EURASIP J. Image Video Process..

[11]  F. Boray Tek,et al.  Occluded face recognition based on Gabor wavelets , 2002, Proceedings. International Conference on Image Processing.

[12]  Matti Pietikäinen,et al.  Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features , 2009, SCIA.

[13]  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.

[14]  Michele Nappi,et al.  Robust Face Recognition after Plastic Surgery Using Local Region Analysis , 2011, ICIAR.

[15]  Michele Nappi,et al.  Robust face recognition after plastic surgery using region-based approaches , 2015, Pattern Recognit..

[16]  Kilian Q. Weinberger,et al.  Learning a kernel matrix for nonlinear dimensionality reduction , 2004, ICML.

[17]  Maria Asuncion Vicente,et al.  Interest-Point based Face Recognition System , 2010 .

[18]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[19]  Lianwen Jin,et al.  Discriminative information preservation for face recognition , 2012, Neurocomputing.

[20]  A. Majumdar,et al.  Face Recognition by Multi-resolution Curvelet Transform on Bit Quantized Facial Images , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[21]  Stephen Lin,et al.  Graph Embedding and Extensions: A General Framework for Dimensionality Reduction , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[23]  Shih-Fu Chang,et al.  Image Retrieval: Current Techniques, Promising Directions, and Open Issues , 1999, J. Vis. Commun. Image Represent..

[24]  Weiguo Fan,et al.  Effective and efficient dimensionality reduction for large-scale and streaming data preprocessing , 2006, IEEE Transactions on Knowledge and Data Engineering.

[25]  Xueming Qian,et al.  PLBP: An effective local binary patterns texture descriptor with pyramid representation , 2011, Pattern Recognit..

[26]  Arun Ross,et al.  Mitigating effects of plastic surgery: Fusing face and ocular biometrics , 2012, 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[27]  B. Prabhakaran,et al.  A novel method for post-surgery face recognition using sum of facial parts recognition , 2014, IEEE Winter Conference on Applications of Computer Vision.

[28]  Himanshu S. Bhatt,et al.  Evolutionary granular approach for recognizing faces altered due to plastic surgery , 2011, Face and Gesture 2011.

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

[30]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

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

[32]  Tieniu Tan,et al.  Histograms of Gabor Ordinal Measures for face representation and recognition , 2012, 2012 5th IAPR International Conference on Biometrics (ICB).

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

[34]  K. R. Singh,et al.  A New Approach to Local Plastic Surgery Face Recognition Using Near Sets , 2010 .

[35]  Hong Yan,et al.  A study on the discriminating characteristics of Gabor phase-face and an improved method for face recognition , 2016, Int. J. Mach. Learn. Cybern..

[36]  Cong Geng,et al.  Fully automatic face recognition framework based on local and global features , 2013, Machine Vision and Applications.

[37]  Mohammed Bennamoun,et al.  Linear Regression for Face Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Massimo Tistarelli,et al.  Structural Similarity based image quality map for face recognition across plastic surgery , 2013, 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[39]  Domingo Mery,et al.  Face Recognition with Local Binary Patterns, Spatial Pyramid Histograms and Naive Bayes Nearest Neighbor Classification , 2009, 2009 International Conference of the Chilean Computer Science Society.

[40]  Bernt Schiele,et al.  Recognition without Correspondence using Multidimensional Receptive Field Histograms , 2004, International Journal of Computer Vision.

[41]  Shutao Li,et al.  An Efficient Dictionary Learning Algorithm and Its Application to 3-D Medical Image Denoising , 2012, IEEE Transactions on Biomedical Engineering.

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

[43]  Mykola Pechenizkiy,et al.  The Impact of Feature Extraction on the Performance of a Classifier: kNN, Naïve Bayes and C4.5 , 2005, Canadian Conference on AI.

[44]  Heng Tao Shen,et al.  Face Identification with Second-Order Pooling , 2014, ArXiv.

[45]  Himanshu S. Bhatt,et al.  Plastic Surgery: A New Dimension to Face Recognition , 2010, IEEE Transactions on Information Forensics and Security.

[46]  Alice Caplier,et al.  Enhanced Patterns of Oriented Edge Magnitudes for Face Recognition and Image Matching , 2012, IEEE Transactions on Image Processing.

[47]  Trevor Darrell,et al.  Heavy-tailed Distances for Gradient Based Image Descriptors , 2011, NIPS.

[48]  Weilin Huang,et al.  Nonlinear Dimensionality Reduction for Face Recognition , 2009, IDEAL.

[49]  Patrick J. Flynn,et al.  A sparse representation approach to face matching across plastic surgery , 2012, 2012 IEEE Workshop on the Applications of Computer Vision (WACV).

[50]  Y. V. Venkatesh,et al.  Facial expression recognition using radial encoding of local Gabor features and classifier synthesis , 2012, Pattern Recognit..

[51]  Zhenhua Guo,et al.  A Completed Modeling of Local Binary Pattern Operator for Texture Classification , 2010, IEEE Transactions on Image Processing.

[52]  Joachim M. Buhmann,et al.  Distortion Invariant Object Recognition in the Dynamic Link Architecture , 1993, IEEE Trans. Computers.

[53]  Mikhail Belkin,et al.  Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[54]  I. Jolliffe.  Principal Component Analysis , 2002 .

[55]  WenAn Tan,et al.  Gabor feature-based face recognition using supervised locality preserving projection , 2007, Signal Process..

[56]  Lei Wang,et al.  Feature selection via simultaneous sparse approximation for person specific face verification , 2011, 2011 18th IEEE International Conference on Image Processing.

[57]  Ghazali Sulong,et al.  Face recognition via edge-based Gabor feature representation for plastic surgery-altered images , 2014, EURASIP J. Adv. Signal Process..

[58]  Yanchun Zhang,et al.  A fast and robust face recognition approach combining Gabor learned dictionaries and collaborative representation , 2016, Int. J. Mach. Learn. Cybern..

[59]  Yang Yang,et al.  Face image classification by pooling raw features , 2014, Pattern Recognit..

[60]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[61]  Hong Yan,et al.  An Empirical Study on the Characteristics of Gabor Representations for Face Recognition , 2009, Int. J. Pattern Recognit. Artif. Intell..

[62]  Peter H. N. de With,et al.  Robust face recognition algorithm for identifition of disaster victims , 2013, Electronic Imaging.

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

[64]  Ole Christensen,et al.  Gabor frames with reduced redundancy , 2009 .

[65]  Kun Zhou,et al.  Locality Sensitive Discriminant Analysis , 2007, IJCAI.

[66]  ZhangLei,et al.  Gabor feature based robust representation and classification for face recognition with Gabor occlusion dictionary , 2013 .

[67]  Simon C. K. Shiu,et al.  Gabor feature based robust representation and classification for face recognition with Gabor occlusion dictionary , 2013, Pattern Recognit..