Abstract: Objective The aim is to investigate the impact of the quality and image restoration methods of small face sample on the performance of facial recognition systems, in order to improve the effectiveness of forensic appraisal recognition. Methods This paper focused on the impact of few-shot face image inpainting on face recognition technology in natural scenes. We selected few-shot face images from the Labeled Faces in the Wild database. Then, we used Gaussian filtering technology on these images. This simulated the quality of real-life blurred face images. At the same time, different face image inpainting methods based on deep neural networks were used to repair the blurred face images. The influence of different few-shot face images quality and different inpainting methods on the recognition performance of face recognition was studied by statistical analysis. Results The face image inpainting technology enhanced the clarity of small face image samples. Moreover, the technology boosted the visual recognition of identity information in these images. However, the repaired face images negatively affected face recognition. Conclusion Face image inpainting technology plays a positive role in portrait identification, but it should be used more carefully when be applied in face recognition.

Key words: face image inpainting, face recognition, portrait identification, small face sample

摘要： 目的 旨在研究小样本人脸图像的质量及修复方法对人脸识别系统性能的影响，以提升司法鉴定识别效果。 方法 研究自然场景中小样本人脸图像修复对人脸识别技术的影响，小样本人脸图像从Labeled Faces in the Wild数据库中选取，并采用高斯滤波技术进行处理以模拟真实人脸图像质量。同时，使用基于深度神经网络的不同修复方法对模糊人脸图像进行高清修复，通过统计量化分析方法研究不同小样本人脸图像质量和修复方法对人脸识别系统识别性能的影响。结果 人脸图像修复技术可以极大地提高小样本人脸图像的清晰度和身份信息视觉辨识度，然而，修复后的人脸图像在人脸识别系统中却产生了负面作用。结论 在人像鉴定中，人脸图像修复技术具有积极作用，但在人脸识别技术应用中，应谨慎使用。

关键词: 人脸图像修复, 人脸识别, 人像鉴定, 小样本人脸