Face images captured in unconstrained settings may suffer from one or multiple degradations, which would degrade the visual aesthetics of images and the performance of face recognition methods. However, many current methods only focus on a specific degradation or restoring the images without considering face identity. To address these problems, an identity-preservation-based deep learning method is proposed for super-resolving blurry face images. First, an extra recognition module is designed and integrated with the restoration module to extract different levels of identity-related and semantic features. Second, an assemble loss function is developed to use the identity preservation information as regularization and prior to guide the restoration and recognition process. Finally, qualitative and quantitative evaluations are conducted to demonstrate the effectiveness of the proposed method for face recovery and face recognition. The results indicate that facial identity can serve as an effective prior to face image restoration.

在不受限制的环境中捕获的面部图像可能会遭受一种或多种降级，这会降低图像的视觉美感和面部识别方法的性能。然而，许多当前方法仅关注特定的降级或恢复图像，而没有考虑面部识别。为了解决这些问题，提出了一种基于身份保存的深度学习方法，用于超分辨模糊人脸图像。首先，设计了一个额外的识别模块，并将其与恢复模块集成在一起，以提取不同级别的身份相关和语义特征。其次，开发了一个组装丢失功能，以将身份保存信息用作正则化并在指导恢复和识别过程之前使用。最后，进行了定性和定量评估，以证明所提出的方法对于面部恢复和面部识别的有效性。结果表明，面部识别可以在面部图像恢复之前起到有效的作用。