Decision optimization for face recognition based on an alternate correlation plane quantification metric

作     者：Alfalou, A. Brosseau, C. Katz, P. Alam, M. S. 

作者机构：Univ Brest Univ Europeenne Bretagne Lab STICC F-29238 Brest 3 France ISEN Brest Dept Optoelect L BISEN F-29228 Brest 2 France Univ S Alabama Dept Elect & Comp Engn Mobile AL 36688 USA 

出 版 物：《OPTICS LETTERS》 (光学快报)

年 卷 期：2012年第37卷第9期

页      面：1562-1564页

核心收录：

学科分类：070207[理学-光学] 07[理学] 08[工学] 0803[工学-光学工程] 0702[理学-物理学] 

主　　题：Composite materials Correlators Face recognition Image recognition algorithms Noise reduction Phase only filters 

摘      要：We consider a new approach for enhancing the discrimination performance of the VanderLugt correlator. Instead of trying to optimize the correlation filter, or propose a new decision correlation peak detection criterion, we propose herein to denoise the correlation plane before applying the peak-to-correlation energy (PCE) criterion. For that purpose, we use a linear functional model to express a given correlation plane as a linear combination of the correlation peak, noise, and residual components. The correlation peak is modeled using an orthonormalized function and the singular value decomposition method. A set of training correlation planes is then selected to create the correlation noise components. Finally, an optimized correlation plane is reconstructed while discarding the noise components. Independently of the filter correlation used, this technique denoises the correlation plane by lowering the correlation noise magnitude in case of true correlation and decreases the false alarm rate when the target image does not belong to the desired class. Test results are presented, using a composite filter and a face recognition application, to verify the effectiveness of the proposed technique. (C) 2012 Optical Society of America