An efficient classification method based on principal component and sparse representation

作者：Zhai, Lin; Fu, Shujun^*; Zhang, Caiming; Liu, Yunxian; Wang, Lu; Liu, Guohua; Yang, Mingqiang

来源：SpringerPlus, 2016, 5(1): 832.

DOI：10.1186/s40064-016-2511-z

• Palmprint recognition
• Image classification
• Principal component analysis
• Sparse representation
• Subspace optimization

As an important application in optical imaging, palmprint recognition is interfered by many unfavorable factors. An effective fusion of blockwise bi-directional two-dimensional principal component analysis and grouping sparse classification is presented. The dimension reduction and normalizing are implemented by the blockwise bi-directional two-dimensional principal component analysis for palmprint images to extract feature matrixes, which are assembled into an overcomplete dictionary in sparse classification. A subspace orthogonal matching pursuit algorithm is designed to solve the grouping sparse representation. Finally, the classification result is gained by comparing the residual between testing and reconstructed images. Experiments are carried out on a palmprint database, and the results show that this method has better robustness against position and illumination changes of palmprint images, and can get higher rate of palmprint recognition.

• 出版日期2016-6-22
• 单位山东财经大学; 山东大学