Thaipisutikul Tipajin, Tatiyamaneekul Phonarnun, Lin Chih-Yang, Tuarob Suppawong
Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, Thailand.
Department of Electrical Engineering, Yuan Ze University, Taoyuan, Taiwan.
J Ambient Intell Humaniz Comput. 2022 Sep 19:1-14. doi: 10.1007/s12652-022-04380-0.
The widespread occurrences of airborne outbreaks (e.g., COVID-19) and pollution (e.g., PM2.5) have urged people in the affected regions to protect themselves by wearing face masks. In certain areas, wearing masks amidst such health-endangering times is even enforced by law. While most people wear masks to guard themselves against airborne substances, some exploit such excuses and use face masks to conceal their identity for criminal purposes such as shoplifting, robbery, drug transport, and assault. While automatic face recognition models have been proposed, most of these models aim to identify clear, unobstructed faces for authentication purposes and cannot effectively handle cases where masks cover most facial areas. To mitigate such a problem, this paper proposes a deep-learning-based feature-fusion framework, , that combines additional demographic-estimated features such as age, gender, and race into the underlying facial representation to compensate for the information lost due to mask obstruction. Given an image of a masked face, our system recommends a ranked list of potential identities of the person behind the mask. Empirical results show that the best configuration of our proposed framework can recognize bare faces and masked faces with the accuracy of 99.34% and 97.65% in terms of Hit@10, respectively. The proposed framework could greatly benefit high-recall facial identity recognition applications such as identifying potential suspects from CCTV or passers-by's cameras, especially during crisis times when people commonly cover their faces with protective masks.
空气传播疫情（如新冠疫情）和污染（如PM2.5）的广泛发生，促使受灾地区的人们通过佩戴口罩来保护自己。在某些地区，在这种危及健康的时期佩戴口罩甚至已被法律强制执行。虽然大多数人戴口罩是为了防范空气中的物质，但有些人利用这种借口，使用口罩来隐藏身份以达到犯罪目的，如盗窃、抢劫、运输毒品和袭击。虽然已经提出了自动人脸识别模型，但这些模型大多旨在识别清晰、无遮挡的面部以用于身份验证，无法有效处理口罩覆盖大部分面部区域的情况。为缓解这一问题，本文提出了一种基于深度学习的特征融合框架，即 ，该框架将年龄、性别和种族等额外的人口统计学估计特征合并到基础面部表示中，以补偿因口罩遮挡而丢失的信息。给定一张戴口罩面部的图像，我们的系统会推荐一个戴口罩者潜在身份的排序列表。实证结果表明，我们提出的框架的最佳配置在Hit@10方面分别能以99.34%和97.65%的准确率识别裸脸和戴口罩的脸。所提出的框架可极大地惠及高召回率的面部身份识别应用，如从闭路电视或路人相机中识别潜在嫌疑人，尤其是在人们通常用防护口罩遮住脸的危机时期。
