As a widely used loss function in deep face recognition, the softmax loss cannot guarantee that the minimum positive sample-to-class similarity is larger than the maximum negative sample-to-class similarity. As a result, no unified threshold is available to separate positive sample-to-class pairs from negative sample-to-class pairs. To bridge this gap, we design a UCE (Unified Cross-Entropy) loss for face recognition model training, which is built on the vital constraint that all the positive sample-to-class similarities shall be larger than the negative ones. Our UCE loss can be integrated with margins for a further performance boost. The face recognition model trained with the proposed UCE loss, UniFace, was intensively evaluated using a number of popular public datasets like MFR, IJB-C, LFW, CFP-FP, AgeDB, and MegaFace. Experimental results show that our approach outperforms SOTA methods like SphereFace, CosFace, ArcFace, Partial FC, etc. Especially, till the submission of this work (Mar. 8, 2023), the proposed UniFace achieves the highest TAR@MR-All on the academic track of the MFR-ongoing challenge. $\color{Blue}{\mathbf{Code}}$ is publicly available.

Sample-to-class-based face recognition models can not fully explore the cross-sample relationship among large amounts of facial images, while sample-to-sample-based models require sophisticated pairing processes for training. Furthermore, neither method satisfies the requirements of real-world face verification applications, which expect a unified threshold separating positive from negative facial pairs. In this paper, we propose a unified threshold integrated sample-to-sample based loss (USS loss), which features an explicit unified threshold for distinguishing positive from negative pairs. Inspired by our USS loss, we also derive the sample-to-sample based softmax and BCE losses, and discuss their relationship. Extensive evaluation on multiple benchmark datasets, including MFR, IJB-C, LFW, CFP-FP, AgeDB, and MegaFace, demonstrates that the proposed USS loss is highly efficient and can work seamlessly with sample-to-class-based losses. The embedded loss (USS and sample-to-class Softmax loss) overcomes the pitfalls of previous approaches and the trained facial model UniTSFace exhibits exceptional performance, outperforming state-of-the-art methods, such as CosFace, ArcFace, VPL, AnchorFace, and UNPG. Our code is available at https://github.com/CVI-SZU/UniTSFace.

A number of facial expression recognition algorithms based on hand-crafted features and deep neutral networks have been developed. Motivated by the similarity between the hand-crafted features and features learned by deep network, a new feature loss is proposed to embed the information of hand-crafted features into the training process of network, which tries to reduce the difference between the two features. Based on the feature loss, a general framework for embedding the traditional feature information was developed and tested using CK+, JAFFE and FER2013 datasets. Experimental results show that the proposed network achieves much better accuracy than the original hand-crafted feature and the network without using our feature loss. When compared with other algorithms in literature, our network also achieved the best performance on CK+ dataset, i.e. 97.35% accuracy has been achieved.