Feature-level fusion method based on KFDA for multimodal recognition fusing ear and profile face

A novel method of feature-level fusion based on kernel Fisher discriminant analysis (KFDA) is proposed and applied to fusion of ear and profile face biometrics in this paper. Ear recognition is proved to be a new and promising authentication technique. Because of ear’s special physiological structure and location, it is reasonable to combine ear with profile face for recognition in such scenarios as frontal face images are not available. First, only the face profile-view images are captured for recognition. Then based on KFDA, three feature fusion rules are presented. With the rules, the fusion discriminant vectors of ear and profile face are established and nonlinear feature fusion projection could be implemented. The experimental results show that the method is efficient for feature-level fusion, and the multimodal recognition based on ear and profile face performs better than ear or profile face unimodal biometric recognition. The work provides a new effective approach of non-intrusive biometric recognition.