Towards Automatic Detection of Monkey Faces

An automated monkey face detection system confers distinct advantages in the protection of wild monkeys, sociological studies, monkey feeding and management and so on. The monkey face and human face have similar structures, but still hold some very important differences in appearance. Therefore, whether the mainstream human face detection algorithms can be adapted to the detection of monkey face is still unknown. To investigate this problem, we collected a database of monkey face (with more than 20,000 macaque faces) and conducted several experiments in our database. Experimental results reveal some interesting results. Firstly, the classical Viola-Jones Adaboost algorithm on monkey faces does not work as well as that on human faces. A in-depth study for this result will be given by taking insight into the selected features by Adaboost. In particular, lips and eyebrows are very important to human face recognition. However, the lack of these prominent features in the monkey's face causes the Viola-Jones algorithm to choose more local Haar-like features, resulting in a higher false positive rate. Secondly, the Faster R-CNN works effectively for monkey face detection but requires a large number of training samples. A pre-training with human faces helps to tackle the problem of shortage of monkey faces for training. Above conclusion indicate that an automatic monkey face detector can be learnt from a human face detector, yet a model with complex features should be employed.