KCCA-based technique for profile face identification

During the last two decades, satisfactory results have been obtained for face identification techniques based on frontal pose. However, face identification from uncontrolled pose remains a challenging open problem in biometric recognition. Recently, pose invariant techniques that exploit either 3D scans or 2D images of the same face to generate the corresponding 3D model have emerged. Even if they tolerate pose variability and lead to high identification scores, they have the drawback to be computationally intensive and/or require the cooperation of the individual to be identified. Hence, they are not appropriate for the interesting real-time application of video surveillance. In this paper, we propose a profile face identification method based on correspondence mapping of 2D frontal face images. Kernel canonical correlation analysis (KCCA) is used to learn changeover from the profile pose to the frontal one. To show the effectiveness of our approach, tests are performed on FERET database according to a protocol referred to as leave one out-like protocol (LOOLP). These tests demonstrate that it leads to enhanced scores comparatively to other 2D-based methods proposed in the literature.