Improved principal component analysis and linear regression classification for face recognition

Abstract In this paper, an improved principal component analysis (IPCA) is presented for face feature representation. IPCA is mainly designed to extract the useful information from original face images through reducing the dimension of feature vectors. Linear regression classification (LRC) algorithm is employed to treat the face recognition as a linear regression issue. LRC uses the least-square method to decide the class label with the minimum reconstruction error. Experiments are conducted on the Yale B, CMU_PIE and JAFFE databases. The proposed IPCA algorithm and LRC algorithm achieve better recognition results than that of state-of-the-art algorithms.

[1]  Jason Weston,et al.  Multi-Class Support Vector Machines , 1998 .

[2]  A. Pires,et al.  Robust Linear Discriminant Analysis and the Projection Pursuit Approach , 2003 .

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

[4]  Abdulaziz A. Alorf.  Performance evaluation of the PCA versus improved PCA (IPCA) in image compression, and in face detection and recognition , 2016, 2016 Future Technologies Conference (FTC).

[5]  Jian Yang,et al.  Robust nuclear norm regularized regression for face recognition with occlusion , 2015, Pattern Recognit..

[6]  Jian-Huang Lai,et al.  Face representation using independent component analysis , 2002, Pattern Recognit..

[7]  Mia Hubert,et al.  Fast and robust discriminant analysis , 2004, Comput. Stat. Data Anal..

[8]  Ran He,et al.  Robust spectral regression for face recognition , 2013, Neurocomputing.

[9]  Xiaobo Ren,et al.  Expression recognition based on EPCA and SVM , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[10]  Jun Yu,et al.  Multitask Autoencoder Model for Recovering Human Poses , 2018, IEEE Transactions on Industrial Electronics.

[11]  Geng Yang,et al.  Adaptive linear discriminant regression classification for face recognition , 2016, Digit. Signal Process..

[12]  Yuxiao Hu,et al.  Face recognition using Laplacianfaces , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Fei Gao,et al.  Deep Multimodal Distance Metric Learning Using Click Constraints for Image Ranking , 2017, IEEE Transactions on Cybernetics.

[14]  Shang-Hong Lai,et al.  A novel robust kernel for appearance-based learning , 2008, 2008 19th International Conference on Pattern Recognition.

[15]  Xuelong Li,et al.  Geometric Mean for Subspace Selection , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  P. Roth,et al.  SURVEY OF APPEARANCE-BASED METHODS FOR OBJECT RECOGNITION , 2008 .

[17]  Sanja Fidler,et al.  Combining reconstructive and discriminative subspace methods for robust classification and regression by subsampling , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[19]  Michael J. Lyons,et al.  Automatic Classification of Single Facial Images , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  H. B. Nielsen,et al.  Finite alogorithms for robust linear regression , 1990 .

[21]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[22]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[23]  Abdulrahman Alkandari,et al.  Principle Component Analysis algorithm (PCA) for image recognition , 2015, 2015 Second International Conference on Computing Technology and Information Management (ICCTIM).

[24]  Xudong Jiang,et al.  Eigenfeature Regularization and Extraction in Face Recognition , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Sang Uk Lee,et al.  Face recognition using face-ARG matching , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[27]  Jia-tao Song,et al.  Expression Recognition Based on Genetic Algorithm and SVM , 2009, ICFIE.

[28]  Bruce A. Draper,et al.  Analyzing PCA-based Face Recognition Algorithms: Eigenvector Selection and Distance Measures , 2003 .

[29]  Ke Lu,et al.  Multiview Hessian regularized logistic regression for action recognition , 2015, Signal Process..

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

[31]  Guillermo Sapiro,et al.  Sparse Representation for Computer Vision and Pattern Recognition , 2010, Proceedings of the IEEE.

[32]  Mohammed Bennamoun,et al.  Face identification using linear regression , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

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

[34]  Ke Lu,et al.  $p$-Laplacian Regularized Sparse Coding for Human Activity Recognition , 2016, IEEE Transactions on Industrial Electronics.

[35]  Václav Hlavác,et al.  Multi-class support vector machine , 2002, Object recognition supported by user interaction for service robots.

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

[37]  Ashok Samal,et al.  Automatic recognition and analysis of human faces and facial expressions: a survey , 1992, Pattern Recognit..

[38]  P. J. Rousseeuw,et al.  Integrating a high-breakdown option into discriminant analysis in exploration geochemistry , 1992 .

[39]  Majid Ahmadi,et al.  A Human Face Recognition System Using Neural Classifiers , 2007, Computer Graphics, Imaging and Visualisation (CGIV 2007).