Towards automated visual identification of primates using face recognition
In this paper we propose and evaluate a face recognition approach for the individual identification of great apes. We extend our previous work to a more automatic approach using an unsupervised face alignment method known as congealing instead of a projective transform based on manually annotated facial feature points. Furthermore we present an improved version of the Randomfaces approach, called Hybridfaces, which complements the global recognition results with information obtained from local facial regions. We evaluate our approach on three publicly available primate databases of captive chimpanzees, free-living chimpanzees, and free-living western lowland gorillas. Our proposed framework shows promising results and the Hybridfaces approach clearly outperforms the previously used basic Randomfaces method on all three datasets.