New Method using Feature Level Image Fusion and Entropy Component Analysis for Multimodal Human Face Recognition

摘要

Visual and infrared cameras have complementary properties and using them together may increase the performance of human face recognition. This study presents a new efficient method for face recognition which fusing the complementary information from both domains. The fused image is obtained by a new image fusion method based on region segmentation and PCNN for the first step. In the second step, features of the fused images are extracted by ECA and 2DECA according to the entropy contribution. The method has been tested on OTCBVS database. Comparison of the experimental results shows that the proposed approach performs significantly well in face recognition.