Application of two-dimensional canonical correlation analysis for face image processing and recognition

Paper presents the method of two-dimensional canonical correlation analysis (2DCCA) applied to image processing and biometrics. Method is based on representing the image as the sets of its rows (r) and columns (c) and implementation of CCA using these sets (for this reason we named the method as CCArc). CCArc features simple implementation and lower complexity than other known approaches. In applications to biometrics CCArc is suitable to solving the problems when dimension of images (dimension of feature space) is greater than number of images, i.e. when Small Sample Size (SSS) problem exists.High efficiency of CCArc method is demonstrated for a number of computer experiments. Experiments are described by means of compact notations that simplify use of results in the framework of meta-analysis.

[1]  Seungjin Choi,et al.  Two-Dimensional Canonical Correlation Analysis , 2007, IEEE Signal Processing Letters.

[2]  H. Hotelling.  Relations Between Two Sets of Variates , 1936 .

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

[4]  M. Szaber,et al.  Systemy rozpoznawania twarzy dla obrazów widzialnych i podczerwieni z wykorzystaniem CCA , 2008 .

[5]  Shaogang Gong,et al.  Fusing gait and face cues for human gender recognition , 2008, Neurocomputing.

[6]  Horst Bischof,et al.  Fast Active Appearance Model Search Using Canonical Correlation Analysis , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  A. Anthony Irudhayaraj,et al.  Biometric system , 2011, 2011 3rd International Conference on Electronics Computer Technology.

[8]  Georgy Kukharev,et al.  Facial images dimensionality reduction and recognition by means of 2DKLT , 2007 .

[9]  Dong Yi,et al.  Face Matching Between Near Infrared and Visible Light Images , 2007, ICB.

[10]  C. Zou,et al.  2DCCA: A Novel Method for Small Sample Size Face Recognition , 2007, 2007 IEEE Workshop on Applications of Computer Vision (WACV '07).