Facial recognition is fundamental for a wide variety of security systems operating in real-time applications. In video surveillance based face recognition, face images are typically captured over multiple frames in uncontrolled conditions; where head pose, illumination, shadowing, motion blur and focus change over the sequence. We can generalize that the three fundamental operations involved in the facial recognition tasks: face detection, face alignment and face recognition. This study presents comparative benchmark tables for the state-of-art face recognition methods by testing them with same backbone architecture in order to focus only on the face recognition solution instead of network architecture. For this purpose, we constructed a video surveillance dataset of face IDs that has high age variance, intra-class variance (face make-up, beard, etc.) with native surveillance facial imagery data for evaluation. On the other hand, this work discovers the best recognition methods for different conditions like non-masked faces, masked faces, and faces with glasses.

该研究针对人脸识别中的关键问题：人脸检测、人脸对齐和人脸识别，通过在同一骨干架构下进行面部识别方法的比较基准测试，提出了最新的人脸识别状态，针对红外图像、无遮挡、带口罩、戴眼镜等情况对不同的识别算法进行了评估。

视频监控场景下人脸识别方法的比较分析