Face Clustering Based on Fusion of Face Tracking and Optimization
Abstract
Face clustering algorithms play an important role in face recognition and data analysis. Since the slow speed and high complexity face clustering algorithm of large-scale data in the face recognition system, we propose a face clustering algorithm combining face tracking and optimization. According to the face image and coordinates obtained by the face detection algorithm, we create face tracks. Then using the face quality assessment algorithm to select the face image with better facial posture and finer image from one of those face tracks and as the representative picture of the face track. Combining the similarity matrix of the face representative image and constraint matrix between face tracks, we can use unsupervised clustering algorithm to cluster representative images of those faces and get better clustering results. Experimental results show that our method can quickly and effectively cluster face images compared with other algorithms.
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Index Terms
- Face Clustering Based on Fusion of Face Tracking and Optimization
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Published In
October 2020
552 pages
ISBN:9781450387835
DOI:10.1145/3436369
Copyright © 2020 ACM.
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- Beijing University of Technology
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 January 2021
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- Research-article
- Research
- Refereed limited
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- the National Natural Science Foundation of China
- the Fujian Provincial Natural Science Foundation of China
Conference
ICCPR 2020
ICCPR 2020: 2020 9th International Conference on Computing and Pattern Recognition
October 30 - November 1, 2020
Xiamen, China
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