Identification of great apes using face recognition

Abstract

In recent years, thousands of species populations declined catastrophically leaving many species on the brink of extinction. Several biological studies have shown that especially primates like chimpanzees and gorillas are threatened. An essential part of effective biodiversity conservation management is population monitoring using remote camera devices. However, due to the large amount of data, the manual analysis of video recordings is extremely time consuming and highly cost intensive. Consequently, there is a high demand for automatic analytical routine procedures using computer vision techniques to overcome this issue. In this paper we present a technique for the identification of great apes, in particular chimpanzees, using state-of-the-art algorithms for human face recognition in combination with several classification schemes. For benchmark purposes we provide a publicly available dataset of captive chimpanzees. In our experiments we applied several common techniques like the well known Eigenfaces, Fisherfaces, Laplacianfaces and Randomfaces approaches to identify individuals. We compare all of these methods in combination with the classification approaches Nearest Neighbor (NN), Support Vector Machine (SVM) and a new concept for face recognition, Sparse Representation Classification (SRC) based on Compressive Sensing (CS).