Face recognition is a core task in computer vision designed to identify and authenticate individuals by analyzing facial patterns and features. This field intersects with artificial intelligence image processing and machine learning with applications in security authentication and personalization. Traditional approaches in facial recognition focus on capturing facial features like the eyes, nose and mouth and matching these against a database to verify identities. However challenges such as high false positive rates have persisted often due to the similarity among individuals facial features. Recently Contrastive Language Image Pretraining (CLIP) a model developed by OpenAI has shown promising advancements by linking natural language processing with vision tasks allowing it to generalize across modalities. Using CLIP's vision language correspondence and single-shot finetuning the model can achieve lower false positive rates upon deployment without the need of mass facial features extraction. This integration demonstrating CLIP's potential to address persistent issues in face recognition model performance without complicating our training paradigm.

翻译：人脸识别是计算机视觉中的核心任务，旨在通过分析面部模式和特征来识别和验证个体身份。该领域与人工智能图像处理和机器学习交叉，在安全认证与个性化服务中具有广泛应用。传统人脸识别方法侧重于捕获眼睛、鼻子和嘴巴等面部特征，并通过与数据库比对来验证身份。然而，由于个体间面部特征的相似性，高误报率等挑战长期存在。近期，由OpenAI开发的对比语言-图像预训练（CLIP）模型通过将自然语言处理与视觉任务相连接，展现出跨模态泛化的潜力，取得了显著进展。利用CLIP的视觉-语言对应能力与单次微调，该模型在部署时无需大规模面部特征提取即可实现更低的误报率。这种集成方案展现了CLIP在解决人脸识别模型性能长期难题方面的潜力，同时无需复杂化训练范式。