Cross grouping strategy based 2DPCA method for face recognition |
| |
Affiliation: | 1. Electric and Electronics Engineering Department, Bilecik Şeyh Edebali University, Turkey;2. Computer Engineering Department, Dumlupınar University, Turkey;1. School of Computer Science and Technology, Xidian University, Xi’an, China;2. School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China;3. School of Software, Xidian University, Xi’an, China;1. School of Information Science & Engineering, Central South University, Changsha, Hunan 410083, China;2. Research Organization of Information and Systems, 4-3-13 Toranomon, Minato-ku, Tokyo 105-0001, Japan;3. The Institute of Statistical Mathematics, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan;4. Hunan Province Higher Education Key Laboratory of Power System Safety Operation and Control, Changsha University of Science and Technology, Changsha, Hunan 410004, China;5. School of Business, Central South University, Changsha, Hunan 410083, China;6. Collaborative Innovation Center of Resource-Conserving & Environment-Friendly Society and Ecological Civilization, Changsha, Hunan 410083, China;1. Department of Electrical Engineering, Asansol Engineering College, Asansol, West Bengal, India;2. Department of Electrical Engineering, Indian School of Mines, Dhanbad, Jharkhand, India;3. Department of Electrical Engineering, National Institute of Technology, Durgapur, West Bengal, India;1. Samsung Electronics, Republic of Korea;2. Intelligent Systems and Emotional Engineering (ISEE) Laboratory, Department of Mechatronics Engineering, Chungnam National University, Republic of Korea |
| |
Abstract: | Grouping strategy exactly specifies the form of covariance matrix, therefore it is very essential. Most 2DPCA methods use the original 2D image matrices to form the covariance matrix which actually means that the strategy is to group the random variables by row or column of the input image. Because of their grouping strategies these methods have two main drawbacks. Firstly, 2DPCA and some of its variants such as A2DPCA, DiaPCA and MatPCA preserve only the covariance information between the elements of these groups. This directly implies that 2DPCA and these variants eliminate some covariance information while PCA preserves such information that can be useful for recognition. Secondly, all the existing methods suffer from the relatively high intra-group correlation, since the random variables in a row, column, or a block are closely located and highly correlated. To overcome such drawbacks we propose a novel grouping strategy named cross grouping strategy. The algorithm focuses on reducing the redundancy among the row and the column vectors of the image matrix. While doing this the algorithm completely preserves the covariance information of PCA between local geometric structures in the image matrix which is partially maintained in 2DPCA and its variants. And also in the proposed study intra-group correlation is weak according to the 2DPCA and its variants because the random variables spread over the whole face image. These make the proposed algorithm superior to 2DPCA and its variants. In order to achieve this, image cross-covariance matrix is calculated from the summation of the outer products of the column and the row vectors of all images. The singular value decomposition (SVD) is then applied to the image cross-covariance matrix. The right and the left singular vectors of SVD of the image cross-covariance matrix are used as the optimal projective vectors. Further in order to reduce the dimension LDA is applied on the feature space of the proposed method that is proposed method + LDA. The exhaustive experimental results demonstrate that proposed grouping strategy for 2DPCA is superior to 2DPCA, its specified variants and PCA, and proposed method outperforms bi-directional PCA + LDA. |
| |
Keywords: | Facial image recognition Grouping strategy 2DPCA Cross-covariance Singular value decomposition |
本文献已被 ScienceDirect 等数据库收录! |
|