Face Recognition: Primates in the Wild

TL;DR

This paper introduces a new CNN-based primate face recognition system called PrimNet, which outperforms existing methods across multiple species and scenarios, aiding conservation efforts with a mobile app.

Contribution

The paper presents PrimNet, a novel CNN architecture for primate face recognition, evaluated on diverse datasets, and demonstrates its superior performance over existing systems.

Findings

PrimNet outperforms FaceNet, SphereFace, and existing lemur recognition systems.

PrimNet achieves higher accuracy in verification and identification scenarios.

An Android app was developed to assist in real-world primate identification.

Abstract

We present a new method of primate face recognition, and evaluate this method on several endangered primates, including golden monkeys, lemurs, and chimpanzees. The three datasets contain a total of 11,637 images of 280 individual primates from 14 species. Primate face recognition performance is evaluated using two existing state-of-the-art open-source systems, (i) FaceNet and (ii) SphereFace, (iii) a lemur face recognition system from literature, and (iv) our new convolutional neural network (CNN) architecture called PrimNet. Three recognition scenarios are considered: verification (1:1 comparison), and both open-set and closed-set identification (1:N search). We demonstrate that PrimNet outperforms all of the other systems in all three scenarios for all primate species tested. Finally, we implement an Android application of this recognition system to assist primate researchers and conservationists in the wild for individual recognition of primates.