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arXiv · 2103.03503

NPT-Loss: A Metric Loss with Implicit Mining for Face Recognition

Syed Safwan Khalid·Muhammad Awais·Chi-Ho Chan·Zhenhua Feng·Ammarah Farooq·Ali Akbari·Josef Kittler
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Abstract

Face recognition (FR) using deep convolutional neural networks (DCNNs) has seen remarkable success in recent years. One key ingredient of DCNN-based FR is the appropriate design of a loss function that ensures discrimination between various identities. The state-of-the-art (SOTA) solutions utilise normalised Softmax loss with additive and/or multiplicative margins. Despite being popular, these Softmax+margin based losses are not theoretically motivated and the effectiveness of a margin is justified only intuitively. In this work, we utilise an alternative framework that offers a more direct mechanism of achieving discrimination among the features of various identities. We propose a novel loss that is equivalent to a triplet loss with proxies and an implicit mechanism of hard-negative mining. We give theoretical justification that minimising the proposed loss ensures a minimum separability between all identities. The proposed loss is simple to implement and does not require heavy hyper-parameter tuning as in the SOTA solutions. We give empirical evidence that despite its simplicity, the proposed loss consistently achieves SOTA performance in various benchmarks for both high-resolution and low-resolution FR tasks.

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Syed Safwan Khalid, Muhammad Awais, Chi-Ho Chan, Zhenhua Feng, Ammarah Farooq, Ali Akbari, Josef Kittler. 2021-03-05. NPT-Loss: A Metric Loss with Implicit Mining for Face Recognition. https://arxiv.org/abs/2103.03503

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