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.
翻译:面部识别是各种实时应用的安全系统的基础。 在基于视频监视的面部识别中,脸部图像通常在不受控制的条件下被多个框架捕获;头部姿势、照明、阴影、运动模糊和焦点变化在顺序上的变化。我们可以概括地说,面部识别任务涉及的三个基本操作:面部检测、面部对齐和面部识别。本研究用同样的主干结构测试了最先进的面部识别方法的比较基准表,以便只关注面部识别解决方案而不是网络结构。为此目的,我们制作了一个面部识别特征的视频监控数据集,该数据集具有较高的年龄差异、阶级内部差异(面部造型、胡子等)以及用于评价的本地监视面部图像数据。另一方面,这项工作发现对非面部脸、面罩面部和戴眼镜面部等不同条件的最佳识别方法。