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Triplet Transform Learning for Automated Primate Face Recognition
M. Agarwal, S. Sinha, M. Singh, S. Nagpal, ,
Published in IEEE Computer Society
Volume: 2019-September
Pages: 3462 - 3466
Automated primate face recognition has enormous potential in effective conservation of species facing endangerment or extinction. The task is characterized by lack of training data, low inter-class variations, and large intra-class differences. Owing to the challenging nature of the problem, limited research has been performed to automate the process of primate face recognition. In this research, we propose a novel Triplet Transform Learning (TTL) model for learning discriminative representations of primate faces. The proposed model reduces the intra-class variations and increases the inter-class variations to obtain robust sparse representations for the primate faces. It is utilized to present a novel framework for primate face recognition, which is evaluated on the primate dataset, comprising of 80 identities including monkeys, gorillas, and chimpanzees. Experimental results demonstrate the efficacy of the proposed approach, where it outperforms the existing approaches and attains state-of-the-art performance on the primates database. © 2019 IEEE.
About the journal
JournalData powered by TypesetProceedings - International Conference on Image Processing, ICIP
PublisherData powered by TypesetIEEE Computer Society