Facial recognition for primate photo identification

In the ongoing biodiversity crisis many species, particularly primates like chimpanzees or gorillas, are threatened. Therefore, autonomous monitoring techniques become more and more important to protect the remaining populations. However, the manual annotation of images and video sequences is not feasible for such a huge amount of data. Consequently, there is a high demand for automated analytical routine procedures. Recently, computer vision techniques for animal detection and identification are applied to overcome this issue. In this paper we compare several state-of-the-art algorithms for human face recognition for the very new field of primate photo identification. Besides common techniques like Eigenfaces, Fisherfaces, Laplacianfaces as well as more sophisticated approaches like Tensor Subspace Analysis and Volterrafaces, we also use a new concept for face recognition using a randomly generated projection matrix in conjunction with a classifier based on sparse representation. Our experimental results show that the Sparse Representation Classifier using a randomly generated projection matrix outperforms all the other algorithms.