Abstract: Face image deduplication is of great significance to face recognition in intelligent surveillance systems,since face detection in videos will produce a large number of repeated face images.In this paper,a method of face image deduplication in videosby integration of face tracking and clustering is proposed.In a video,use the face detection algorithm in the Multi-task Convolutional Neural Network to extract the face frame and its corresponding coordinates.Face tracking is used to construct the face trajectory and the constraint matrix,and the face quality evaluation algorithm is introduced to select the face pose and image clarity from the face trajectory.An optimal face image is used as a representative of the face trajectory.Combined with the constraint matrix and unsupervised clustering algorithm,the representative images of the faces are clustered to obtain the face image of the same person.Finally,the face image of each person is evaluated again to obtain the deduplication.Experimental results show that,through face tracking and unsupervised clustering,the face image deduplication method in videos can quickly and efficiently obtain high-quality face images that are not repeated for each person from a video.

Key words: Face clustering, Face detection, Face track