A Cluster-Matching-Based Method for Video Face Recognition. Mendes, P. R. C.; Busson, A. J. G.; Colcher, S.; Schwabe, D.; Guedes, Á. L. V.; and Laufer, C. In Proceedings of the Brazilian Symposium on Multimedia and the Web, pages 97–104, 2020. Association for Computing Machinery.
A Cluster-Matching-Based Method for Video Face Recognition [link]Paper  A Cluster-Matching-Based Method for Video Face Recognition [link]Year  abstract   bibtex   
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.
@inproceedings{mendes_cluster-matching-based_2020,
	location = {New York, {NY}, {USA}},
	title = {A Cluster-Matching-Based Method for Video Face Recognition},
	isbn = {978-1-4503-8196-3},
	url = {https://doi.org/10.1145/3428658.3430967},
	abstract = {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.},
	pages = {97--104},
	booktitle = {Proceedings of the Brazilian Symposium on Multimedia and the Web},
	publisher = {Association for Computing Machinery},
	author = {Mendes, Paulo Renato C. and Busson, Antonio José G. and Colcher, Sérgio and Schwabe, Daniel and Guedes, Álan Lívio V. and Laufer, Carlos},
	urlyear = {2021},
	year = {2020},
	keywords = {Clustering, Deep learning, Face recognition},
}
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