3D-aided profile-based face recognition

The silhouette of the face profile is a well-known biometric that is already in use in face recognition research. One of the challenges for successful employment of this biometric is the sensitivity of its geometry to face rotation. In this paper, we introduce a new method that improves robustness to rotation. We achieve this by exploring the feature space of profiles under various rotations with the aid of a 3D face model. Based on fiducial points on the profile silhouette, we extract a set of rotation-, translation- and scale-invariant features which are used to design and train a hierarchical pose-identity classifier. In our experiments the classifier is used for the identification of a driver using his/her side-view image. We present our results on a publicly available database.