Analysis Loss Functions for Recognition Accuracy Improvement in Face Recognition
Zhaoyin Yang · ITM Web of Conferences · 2025
In the field of face recognition, improving recognition accuracy is an important research direction. Due to the fact that face images may be disturbed during acquisition, transmission and storage, the recognition algorithms are not robust enough to meet the requirements of practical applications. Therefore, the introduction of an effective loss function is particularly critical. In this paper, the advantages and disadvantages of different loss functions will be derived by analysing the softmax, ArcFace and AdaFace loss functions and comparing the differences between them. Through the analysis, this paper concludes that the loss functions still have different defects in the application of real scenarios and cannot effectively identify data with fewer samples. For example, Arcace loss is not able to perfectly make the samples evenly distributed on the hypersphere in real life. Therefore, in the future design and optimisation of the self-supervised pre-training framework should be strengthened to make a greater improvement in the recognition accuracy of face recognition.