A new method for combined face detection and identification using interest point descriptors

Although face recognition has been an active research area for decades, the general problem is still unsolved. While many methods addressing either face detection or face identification emerged, little attention has been paid to the combination or integration of detection and identification. In this paper we propose a combined method for face detection and identification using SIFT descriptors. This combined method includes an existing detection model and a new identification method based on object class invariants (OCIs), which is invariant to translation, scale, in-plane rotation and small 3D viewpoint changes. These models are combined using a bounding box around the OCI to filter face features for identification. We show the highly competitive performance of the newly developed identification method and the effectiveness of the proposed combination scheme.