A sequential subspace face recognition framework using genetic-based clustering

Different from other classification problems, there are usually a large number of classes in the face recognition. As a result, the recognition accuracy of the traditional subspace face recognition algorithm is unsatisfactory. This paper presents a sequential subspace face recognition framework using an effective genetic-based clustering algorithm (GCA). Firstly, the facial database is decomposed into a double layer database using a face recognition oriented GCA. Then, the face recognition is realized by minimizing the distance measures in a specific cluster as in the traditional subspace face recognition algorithms. The contributions of this study are summarized as follows: 1) The class, i.e., person is regarded as an element in the clustering rather than an image. 2) The proposed GCA uses a novel distance to measure the similarity between a class and the cluster centroids of different clusters. 3) The proposed GCA uses a balance factor to achieve balanced clustering results. Experimental results on the extended Yale-B database indicate that the proposed sequential subspace face recognition framework has higher accuracy compared with the traditional subspace methods and K-mean+traditional subspace methods.

[1]  Milos Oravec,et al.  Face Recognition in Ideal and Noisy Conditions Using Support Vector Machines, PCA and LDA , 2010 .

[2]  James Ze Wang,et al.  Real-Time Computerized Annotation of Pictures , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Nenghai Yu,et al.  Annotating personal albums via web mining , 2008, ACM Multimedia.

[4]  Yi Yang,et al.  Image Clustering Using Local Discriminant Models and Global Integration , 2010, IEEE Transactions on Image Processing.

[5]  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..

[6]  Vytautas Perlibakas,et al.  Distance measures for PCA-based face recognition , 2004, Pattern Recognit. Lett..

[7]  Yi Hong,et al.  A comprehensive comparison between real population based tournament selection and virtual population based tournament selection , 2007, 2007 IEEE Congress on Evolutionary Computation.

[8]  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.

[9]  Jian-Huang Lai,et al.  GA-fisher: a new LDA-based face recognition algorithm with selection of principal components , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  Bruce A. Draper,et al.  The CSU Face Identification Evaluation System , 2005, Machine Vision and Applications.

[11]  Chris T. Kiranoudis,et al.  Radial Basis Function Neural Networks Classification for the Recognition of Idiopathic Pulmonary Fibrosis in Microscopic Images , 2008, IEEE Transactions on Information Technology in Biomedicine.

[12]  Vincent S. Tseng,et al.  A Novel Similarity-Based Fuzzy Clustering Algorithm by Integrating PCM and Mountain Method , 2007, IEEE Transactions on Fuzzy Systems.

[13]  K. Kim,et al.  Face recognition using kernel principal component analysis , 2002, IEEE Signal Process. Lett..

[14]  Gregory Shakhnarovich,et al.  Face Recognition in Subspaces , 2011, Handbook of Face Recognition.

[15]  Kotaro Hirasawa,et al.  An efficient preprocessing method for suboptimal route computation , 2011 .

[16]  Xinchao Zhao.  Convergent analysis on evolutionary algorithm with non-uniform mutation , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

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

[18]  Jian Yu,et al.  General C-Means Clustering Model , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

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

[20]  Yixin Chen,et al.  CLUE: cluster-based retrieval of images by unsupervised learning , 2005, IEEE Transactions on Image Processing.

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

[22]  B. Draper,et al.  The CSU Face Identification Evaluation System User ’ s Guide : Version 4 . 0 , 2002 .

[23]  Shingo Mabu,et al.  A Genetic Algorithm Based Clustering Method for Optimal Route Calculation on Multilevel Networks , 2011 .

[24]  Shiri Gordon,et al.  Unsupervised image-set clustering using an information theoretic framework , 2006, IEEE Transactions on Image Processing.

[25]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[26]  Daoqiang Zhang,et al.  A Multiobjective Simultaneous Learning Framework for Clustering and Classification , 2010, IEEE Transactions on Neural Networks.

[27]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.