Improvised contrastive loss for improved face recognition in open-set nature

Abstract

Face recognition models often encounter various unseen domains and environments in real-world applications, leading to unsatisfactory performance due to the open-set nature of face recognition. Models trained on central datasets may exhibit poor generalization when faced with different candidates under varying illumination and blur conditions. In this paper, our goal is to enhance the generalization of face recognition models for diverse target conditions without relying on active or incremental learning. We propose an approach for face recognition that utilizes contrastive learning to synthesize positive and multiple negative samples. To address the combinatorial challenges posed by positive and negative samples, our framework incorporates a combination of contrastive regularizer loss and Arcface loss, along with an effective sampling strategy for batch model learning. We update the model weights by jointly back-propagating contrastive and ArcFace gradients. We validate our method on both generalized and standard face recognition benchmarks dataset namely IJB-B and IJB-C. Series of experimentation revealed the out-performance of proposed framework against other state-of-the-art methods.