Pose-invariant face recognition using facial landmarks and Weber local descriptor

Face recognition across pose is a popular issue in biometrics. Facial rotations caused by pose dramatically enlarge the intra-class variations, which considerably obstructs the performance of the face recognition algorithms. It is advisable to extract more discriminative features to overcome this difficulty. In this paper, we present a simple but efficient feature extraction method based on facial landmarks and multi-scale fusion features. We first extract local features by using Weber local descriptors (WLD) and multi-scale patches centered at predefined facial landmarks, and then construct fusion features by randomly selecting parts of local features. Finally, the classification result is obtained by decision fusion of all local features and fusion features. The proposed method has the following two characteristics: (1) local features around landmarks can well describe the similarity between two images under pose variations and simultaneously reduce redundant information and (2) fusion features constructed by randomly selecting local features from predefined regions further alleviate the influence of pose variations. Extensive experimental results on public face datasets have shown that the proposed method greatly outperforms the previous state-of-the-art algorithms.