Face Recognition Using String Grammar Nearest Neighbor Technique

Face recognition has become one of the important biometrics in many applications. However, there is a problem of collecting more than one image per person in the training data set, the so-called "one sample per person problem". Hence in this paper, we develop a face recognition system with a string grammar nearest neighbor (sgNN) to cope with the problem. We implement our system in three data sets, i.e., ORL, MIT-CBCL, and Georgia Tech databases. The recognition rates of the test data set from three databases are 88.25%, 87.50%, and 70.71%, respectively. 

[1]  David Beymer,et al.  Face recognition from one example view , 1995, Proceedings of IEEE International Conference on Computer Vision.

[2]  Aleix M. Martínez,et al.  Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Zhi-Hua Zhou,et al.  Face recognition from a single image per person: A survey , 2006, Pattern Recognit..

[4]  Xiaolan Fu,et al.  Face Recognition and Micro-expression Recognition Based on Discriminant Tensor Subspace Analysis Plus Extreme Learning Machine , 2014, Neural Processing Letters.

[5]  Alejandro F. Frangi,et al.  Two-dimensional PCA: a new approach to appearance-based face representation and recognition , 2004 .

[6]  James M. Keller,et al.  Fuzzy Models and Algorithms for Pattern Recognition and Image Processing , 1999 .

[7]  Wen Gao,et al.  Face recognition based on face‐specific subspace , 2003, Int. J. Imaging Syst. Technol..

[8]  Yuxiao Hu,et al.  Learning a Spatially Smooth Subspace for Face Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Jianxin Wu,et al.  Face recognition with one training image per person , 2002, Pattern Recognit. Lett..

[10]  Zhi-Hua Zhou,et al.  Making FLDA applicable to face recognition with one sample per person , 2004, Pattern Recognit..

[11]  Anastasios Tefas,et al.  Frontal face authentication using morphological elastic graph matching , 2000, IEEE Trans. Image Process..

[12]  King-Sun Fu,et al.  Syntactic Pattern Recognition And Applications , 1968 .

[13]  Daoqiang Zhang,et al.  Enhanced (PC)2 A for face recognition with one training image per person , 2004, Pattern Recognit. Lett..

[14]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[15]  Bernd Girod,et al.  CHoG: Compressed histogram of gradients A low bit-rate feature descriptor , 2009, CVPR.

[16]  Ah Chung Tsoi,et al.  Face recognition: a convolutional neural-network approach , 1997, IEEE Trans. Neural Networks.

[17]  Hung-Son Le,et al.  Recognizing frontal face images using Hidden Markov models with one training image per person , 2004, ICPR 2004.

[18]  Qin Li,et al.  Orthogonal discriminant vector for face recognition across pose , 2012, Pattern Recognit..

[19]  Bruce A. Draper,et al.  The Good, the Bad, and the Ugly Face Challenge Problem , 2012, Image and Vision Computing.

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

[21]  Jian-Huang Lai,et al.  Component-based LDA method for face recognition with one training sample , 2003, 2003 IEEE International SOI Conference. Proceedings (Cat. No.03CH37443).

[22]  Eleftherios Kayafas,et al.  Vehicle model recognition from frontal view image measurements , 2011, Comput. Stand. Interfaces.

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

[24]  Volker Blanz,et al.  Component-Based Face Recognition with 3D Morphable Models , 2004, CVPR Workshops.

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

[26]  Xiaofei Zhou,et al.  Kernel subclass convex hull sample selection method for SVM on face recognition , 2010, Neurocomputing.

[27]  Sang-Woong Lee,et al.  Face recognition under arbitrary illumination using illuminated exemplars , 2007, Pattern Recognit..

[28]  R. Chellappa,et al.  Subspace Linear Discriminant Analysis for Face Recognition , 1999 .

[29]  Hong Man,et al.  Face recognition based on multi-class mapping of Fisher scores , 2005, Pattern Recognit..

[30]  Hong Yan,et al.  An Analytic-to-Holistic Approach for Face Recognition Based on a Single Frontal View , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[31]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[32]  Xiaoou Tang,et al.  Dual-space linear discriminant analysis for face recognition , 2004, CVPR 2004.

[33]  Matti Pietikäinen,et al.  Face Recognition with Local Binary Patterns , 2004, ECCV.

[34]  Jie Wang,et al.  Selecting discriminant eigenfaces for face recognition , 2005, Pattern Recognit. Lett..

[35]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[36]  Daoqiang Zhang,et al.  A new face recognition method based on SVD perturbation for single example image per person , 2005, Appl. Math. Comput..

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