Simple Triplet Loss Based on Intra/Inter-Class Metric Learning for Face Verification

Zuheng Ming, Joseph Chazalon, Muhammad Muzzamil Luqman, Muriel Visani, Jean-Christophe Burie; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1656-1664

Abstract


Recently, benefiting from the advances of the deep convolution neural networks (CNNs), significant progress has been made in the field of the face verification and face recognition. Specially, the performance of the FaceNet has overpassed the human level performance in terms of the accuracy on the datasets "Labeled Faces in the Wild (LFW)"and "Youtube Faces in the Wild (YTF)". The triplet loss used in the FaceNet has proved its effectiveness for face verification. However, the number of the possible triplets is explosive when using a large scale dataset to train the model. In this paper, we propose a simple class-wise triplet loss based on the intra/inter-class distance metric learning which can largely reduce the number of the possible triplets to be learned. However the simplification of the classic triplet loss function has not degraded the performance of the proposed approach. The experimental evaluations on the most widely used benchmarks LFW and YTF show that the model with the proposed class-wise simple triplet loss can reach the state-of-the-art performance. And the visualization of the distribution of the learned features based on the MNIST dataset has also shown the effectiveness of the proposed method to better separate the classes and make the features more discriminative in comparison with the other state-of-the-art loss function.

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[bibtex]
@InProceedings{Ming_2017_ICCV,
author = {Ming, Zuheng and Chazalon, Joseph and Muzzamil Luqman, Muhammad and Visani, Muriel and Burie, Jean-Christophe},
title = {Simple Triplet Loss Based on Intra/Inter-Class Metric Learning for Face Verification},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}
}