Work

Survey on the Loss Function of Deep Learning in Face Recognition

Year: 2021

Type: article

Abstract: With the continuous development of face recognition network, the selection of loss function plays an increasingly important role in improving accuracy. The loss function of face recognition network needs to minimize the intra-class distance while expanding the inter-class distance. So far, one of our mainstream loss function optimization methods is to add penalty terms, such as orthogonal loss, to further constrain the original loss function. The other is to optimize using the loss based on angular/cosine margin. The last is Triplet loss and a new type of joint optimization based on HST Loss and ACT Loss. In this paper, based on the three methods with good practical performance and the joint optimization method, various loss functions are thoroughly reviewed.

Source: Journal of Information Hiding and Privacy Protection

DOI: 10.32604/jihpp.2021.016835

Authors: Jun Wang, Suncheng Feng, Yong Cheng, Najla Al-Nabhan

Institutions: Nanjing University of Information Science and Technology, King Saud University

Language: English

Topic: Face recognition and analysis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W3157459010

FWCI: 0.45

Cites:

Cited by: 6

Related to: 10