دانلود رایگان مقاله انگلیسی تشخیص چهره خودکار میمون رزوس - الزویر 2018

abstract

Background: Rhesus macaques are widely used in biomedical research. Automated behavior monitoring can be useful in various fields (including neuroscience), as well as having applications to animal welfare but current technology lags behind that developed for other species. One difficulty facing developers is the reliable identification of individual macaques within a group especially as pair- and group-housing of macaques becomes standard. Current published methods require either implantation or wearing of a tracking device. New method: I presentface recognition, in combination with face detection, as a method to non-invasively identify individual rhesus macaques in videos. The face recognition method utilizes local-binary patterns in combination with a local discriminant classification algorithm. Results: A classification accuracy of between 90 and 96% was achieved for four different groups. Group size, number of training images and challenging image conditions such as high contrast all had an impact on classification accuracy. I demonstrate that these methods can be applied in real time using standard affordable hardware and a potential application to studies of social structure. Comparison with existing method(s): Face recognition methods have been reported for humans and other primate species such as chimpanzees but not rhesus macaques. The classification accuracy with this method is comparable to that for chimpanzees. Face recognition has the advantage over other methods for identifying rhesus macaques such as tags and collars of being non-invasive. Conclusions: This is the first reported method for face recognition of rhesus macaques, has high classification accuracy and can be implemented in real time.

4.4. Conclusions

I have shown that it is possible to automatically identify individual macaques by their faces in videos. This can be done in real time and the methods can be used at this time for a range of applications. Future work will include improving the classification accuracy especially under challenging conditions, combining face recognition with tracking to monitor behavior and to investigate other applications of these methods.