A Cluster-Matching-Based Method for Video Face Recognition
This addresses scalability issues in face recognition systems for applications requiring the addition of new people, though it appears incremental.
The paper tackles the problem of scalable video face recognition by proposing a cluster-matching-based method that uses unsupervised learning and a heuristic to identify registered and non-registered persons, achieving a recall of 99.435% and precision of 99.131%.
Face recognition systems are present in many modern solutions and thousands of applications in our daily lives. However, current solutions are not easily scalable, especially when it comes to the addition of new targeted people. We propose a cluster-matching-based approach for face recognition in video. In our approach, we use unsupervised learning to cluster the faces present in both the dataset and targeted videos selected for face recognition. Moreover, we design a cluster matching heuristic to associate clusters in both sets that is also capable of identifying when a face belongs to a non-registered person. Our method has achieved a recall of 99.435% and a precision of 99.131% in the task of video face recognition. Besides performing face recognition, it can also be used to determine the video segments where each person is present.