Head pose estimation and face recognition using a non-linear tensor-based model

Although the ability to estimate the face pose and recognise its identity are common human abilities, they are still a challenge in computer vision context. In this study, the authors aim to overcome these difficulties by learning a non-linear tensor-based model based on multi-linear decomposition. Proposed model maps the high-dimensional image space into low-dimensional pose manifold. For preserving the actual distance along the manifold shape, a graph-based distance measure is proposed. Also, to compensate for the limited number of training poses, mirrored images are added to training ones to improve the recognition accuracy. For performance evaluation of the proposed method, experiments are run on three famous face databases using three different manifold shapes and two different distance measures. Eight training data modes are chosen such that the influential parameters are studied comprehensively. The obtained results confirm the effectiveness of proposed model in achieving high accuracy in pose estimation and multi-view face recognition, even with different training poses for different identities.