论文标题
视频监视场景中面部识别方法的比较分析
A Comparative Analysis of the Face Recognition Methods in Video Surveillance Scenarios
论文作者
论文摘要
面部识别对于在实时应用程序中运行的各种安全系统至关重要。在基于视频监视的面部识别中，通常在不受控制的条件下在多个框架上捕获面部图像。头部摆姿势，照明，阴影，运动模糊和聚焦在序列上的变化。我们可以概括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。这项研究通过使用相同的骨干架构测试，为它们进行了比较基准表，以便将其测试，以便将其专注于面部识别解决方案而不是网络体系结构。为此，我们构建了一个具有较高年龄差异，类内差异（面部化妆，胡须等）的面部ID的视频监视数据集，并使用本机监视面部成像数据进行评估。另一方面，这项工作发现了针对不同条件的最佳识别方法，例如未掩盖的面孔，蒙面的脸和戴眼镜的脸。
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.
