Cosmos-Loss: A Face Representation Approach With Independent Supervision

Cost function is one of the most important topics in face recognition. Classic methods based on anchor-positive-negative sample pairs directly or indirectly have been proved to be effective. Taking advantage of information from sample pair with labels is the key point to make intra-class samples more centralized and inter-class ones more scattered. However, there are some problems to constrain model in these classical approaches, such as sample-pair-combination and the poor information used in parameter updating. This paper proposes a novel method named Cosmos-Loss based on the principle of feature center. It divides the feature space into many subspaces. There is a one-to-one match between each subspace and face identity. All samples belonged to the same subspace decide their feature center. This proposed method uses the information between sample and its own feature center to make inner features more similar and the information between sample and other center to make intra features more dissimilar, instead of the poor information between only two samples. This supervision method could use the information of all samples which belonged to the same center indirectly and make feature distribution smoother. And all experiments show that this proposed method is an effective way to supervise model training independently.