@Article{jihpp.2021.016835,
AUTHOR = {Jun Wang, Suncheng Feng, Yong Cheng, Najla Al-Nabhan},
TITLE = {Survey on the Loss Function of Deep Learning in Face Recognition},
JOURNAL = {Journal of Information Hiding and Privacy Protection},
VOLUME = {3},
YEAR = {2021},
NUMBER = {1},
PAGES = {29--45},
URL = {http://www.techscience.com/jihpp/v3n1/42328},
ISSN = {2637-4226},
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.},
DOI = {10.32604/jihpp.2021.016835}
}