The discriminability of feature representation is the key to open-set face recognition. Previous methods rely on the learnable weights of the classification layer that represent the identities. However, the evaluation process learns no identity representation and drops the classifier from training. This inconsistency could confuse the feature encoder in understanding the evaluation goal and hinder the effect of identity-based methods. To alleviate the above problem, we propose a novel approach namely Contrastive Regularization for Face recognition (CoReFace) to apply image-level regularization in feature representation learning. Specifically, we employ sample-guided contrastive learning to regularize the training with the image-image relationship directly, which is consistent with the evaluation process. To integrate contrastive learning into face recognition, we augment embeddings instead of images to avoid the image quality degradation. Then, we propose a novel contrastive loss for the representation distribution by incorporating an adaptive margin and a supervised contrastive mask to generate steady loss values and avoid the collision with the classification supervision signal. Finally, we discover and solve the semantically repetitive signal problem in contrastive learning by exploring new pair coupling protocols. Extensive experiments demonstrate the efficacy and efficiency of our CoReFace which is highly competitive with the state-of-the-art approaches.

翻译：特征表示的判别性是开集人脸识别的关键。现有方法依赖于表征身份的分类层可学习权重，但评估过程中无需学习身份表征且会移除训练分类器。这种不一致性可能导致特征编码器混淆评估目标，从而削弱基于身份的方法的效果。为缓解上述问题，我们提出一种名为CoReFace（对比正则化人脸识别）的新方法，在特征表示学习中应用图像级正则化。具体而言，我们采用样本引导的对比学习，直接利用图像-图像关系对训练进行正则化，这与评估过程保持一致。为将对比学习融入人脸识别，我们通过增强嵌入向量而非原始图像来避免图像质量退化。接着，我们提出一种面向表示分布的新型对比损失函数，通过引入自适应边际和基于监督的对比掩码，生成稳定的损失值并避免与分类监督信号冲突。最后，我们通过探索新型配对协议，发现并解决了对比学习中语义重复信号的问题。大量实验表明，我们的CoReFace方法兼具高效性与有效性，与最新技术相比极具竞争力。