Minimum Margin Loss for Deep Face Recognition

TL;DR

This paper introduces Minimum Margin Loss (MML), a new loss function for deep face recognition that enhances discriminative features by enlarging margins between class centers, leading to state-of-the-art results.

Contribution

The paper proposes MML, a novel loss function that improves face recognition by increasing margins between class centers, complementing existing loss functions.

Findings

Achieves state-of-the-art performance on MegaFace, LFW, and YTF datasets.

Effectively enlarges margins between class centers, improving discriminative ability.

Demonstrates the effectiveness of MML in deep face recognition tasks.

Abstract

Face recognition has achieved great progress owing to the fast development of the deep neural network in the past a few years. As an important part of deep neural networks, a number of the loss functions have been proposed which significantly improve the state-of-the-art methods. In this paper, we proposed a new loss function called Minimum Margin Loss (MML) which aims at enlarging the margin of those overclose class centre pairs so as to enhance the discriminative ability of the deep features. MML supervises the training process together with the Softmax Loss and the Centre Loss, and also makes up the defect of Softmax + Centre Loss. The experimental results on MegaFace, LFW and YTF datasets show that the proposed method achieves the state-of-the-art performance, which demonstrates the effectiveness of the proposed MML.