Feature fusion method based on KCCA for ear and profile face based multimodal recognition

In this paper, a novel feature fusion method based on kernel canonical correlation analysis (KCCA) is presented and applied to ear and profile face based multimodal biometrics for personal recognition. Ear recognition is proved to be a new and promising authentication technique. The fusion of ear and face biometrics could fully utilize their connection relationship of physiological location, and possess the advantage of recognizing people without their cooperation. First, the profile-view face images including ear part were used for recognition. Then the kernel trick was introduced to canonical correlation analysis (CCA), and the feature fusion method based on KCCA is established. With this method, a kind of nonlinear associated feature of ear and face was proposed for classification and recognition. The result of experiment shows that the method is efficient for feature fusion, and the multimodal recognition based on ear and profile face performs better than ear or profile face unimodal biometric recognition and enlarges the recognition range. The work provides a new effective approach of non-intrusive biometric recognition.