A Survey on Loss Functions for Deep Face Recognition Network

Abstract

With the increased collaboration between humans and robots in daily life, face recognition becomes one of the essential aspects of human-robot interaction (HRI). The robot requires a highly accurate face recognition system to operate in different environments and conditions. The role of achieving high accuracy face recognition is the enhancement of learning discriminative feature representation, which is almost entirely around minimizing the intra-class distance and maximizing the inter-class distance. The loss function is used on deep Convolutional Neural Networks (CNNs) to enhance this discriminative power of the deeply learned features. Softmax loss is one of the most used loss functions in many CNNs. However, softmax loss did not have the sufficient discriminative power needed for face recognition. Recently, many researchers work on developing novel loss functions to improve discriminatory power mainly, the intra-class distance of deep features. This survey paper’s main objective is to compare the multiple loss functions used for deep face recognition networks showing the weakness for each loss function.