Masked Face Recognition with Identification Association
Hong Qi, Zhongyuan Wang, Zheng He, Nanxi Wang, Xin Tian, Tao Lü
Abstract
In the crime scene, criminals often consciously conceal their facial identity through face-masked disguise, which poses a huge challenge to identity recognition. Existing disguised face recognition techniques aiming for light even slight occlusions are completely invalid for face-masked identification. To this end, this paper proposes a masked face recognition method based on person re-identification association, which converts the masked face recognition problem into an association uncovering problem between the masked face and the appearing faces of the same person. Based on the characteristics that person re-identification technique does not rely solely on facial information, it first takes advantages of re-identification to establish the association between face-masked pedestrians and face-unveiled pedestrians. It further provides an effective face image quality assessment to select the most identifiable faces for subsequent recognition from a variety of appearing candidate faces. Finally, the selected high-quality recognizable faces are used to replace masked faces for identification. The comparison experiments with the existing disguise face recognition methods show its superiority in terms of accuracy.