dc.description.abstract | In the new era of the pneumonia epidemic, due to government policies, people often take off their masks temporarily for chatting, eating, and taking pictures, but they forget to put their masks on properly in the next occasion. In this thesis, we design a system by improving the original system [1] that detects people’s mask-wearing status, identifies live and fake faces, estimates people′s gender and age, and enhances the system′s resistance to mask obscuration. The original system [1] was designed with an identification feature that allows the system to recognize faces through a face database even if they are partially obscured, such as masks, sunglasses, hats, and other occlusions, as long as the faces are registered. In this thesis, we continue to extend the functionality of the original system [1] to detect people’s mask-wearing status, determine whether people are wearing masks correctly, and perform a deeper analysis to identify "registered face" for liveness detection, to prevent people from using fake faces (e.g. photos or pictures) to pass. The system can apply gender and age estimation of "unregistered face" to increase the system′s record of strangers on the screen. Then we can use the record of strangers to analysis.
The system consists of three main parts. The first part is the detection of masking status. First, the model detects the face region on the screen and determines which masking state each face belongs to. Then let the identity information in the original system [1] and the mask-wearing status be matched such that the system will recognize the identity and the mask-wearing status for each face. The second part is the liveness detection to understand the "registered face" appearing on the screen by the identity information, and the unmasked area of the face by the mask-wearing status information to obtain the unmasked features of the face and identify the live and fake faces. The third part is the estimation of gender and age. The gender and age of the face are estimated by understanding the "unregistered face" that appears on the screen through the recognition information and the uncovered area of the face through the mask-wearing status information. The system can achieve an overall computing performance of 10 FPS for real-time recognition.
[1] 儲健峰, "基於AI技術之對抗部分遮蔽的即時臉部辨識系統," 碩士論文, 國立中央大學, 2021. | en_US |