Data Fusion for Face Recognition

摘要

Face recognition is an important biometric because of its potential applications in many fields, such as access control, surveillance, and human-computer interface. In this paper, we propose a rule-based face recognition system that fuses the output of two face recognition systems based on principal component analysis (PCA). One system uses the face image while the other use the Radon transform of the same face image. In addition, both systems use the Euclidean distance is the matching criteria. Both systems are trained using the same training images database, and fed with the same test input image at same time and the recognition result of each system is serving as input for the fusion decision stage. The proposed system is found to be better (97% recognition rate for recall and 93% for reject) than either system alone