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An online algorithm for constrained face clustering in videos
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Kulshreshtha, Prakhar and Guha, Tanaya (2018) An online algorithm for constrained face clustering in videos. In: 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece, 7-10 Oct 2018 pp. 2670-2674. ISBN 9781479970629. doi:10.1109/ICIP.2018.8451343 ISSN 2381-8549.
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Official URL: http://dx.doi.org/10.1109/ICIP.2018.8451343
Abstract
We address the problem of face clustering in long, real world videos. This is a challenging task because faces in such videos exhibit wide variability in scale, pose, illumination, expressions, and may also be partially occluded. The majority of the existing face clustering algorithms are offline, i.e., they assume the availability of the entire data at once. However, in many practical scenarios, complete data may not be available at the same time or may be too large to process or may exhibit significant variation in the data distribution over time. We propose an online clustering algorithm that processes data sequentially in short segments of variable length. The faces detected in each segment are either assigned to an existing cluster or are used to create a new one. Our algorithm uses several spatiotemporal constraints, and a convolutional neural network (CNN) to obtain a robust representation of the faces in order to achieve high clustering accuracy on two benchmark video databases (82.1 % and 93.8%). Despite being an online method (usually known to have lower accuracy), our algorithm achieves comparable or better results than state-of-the-art offline and online methods.
| Item Type: | Conference Item (Paper) | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
| Library of Congress Subject Headings (LCSH): | Cluster analysis, Online algorithms, Pattern recognition systems, Human face recognition (Computer science), Neural networks (Computer science) | ||||||
| Publisher: | IEEE | ||||||
| ISBN: | 9781479970629 | ||||||
| ISSN: | 2381-8549 | ||||||
| Book Title: | 2018 25th IEEE International Conference on Image Processing (ICIP) | ||||||
| Official Date: | 8 September 2018 | ||||||
| Dates: |
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| Page Range: | pp. 2670-2674 | ||||||
| DOI: | 10.1109/ICIP.2018.8451343 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Reuse Statement (publisher, data, author rights): | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| Date of first compliant deposit: | 11 October 2018 | ||||||
| Date of first compliant Open Access: | 12 October 2018 | ||||||
| Conference Paper Type: | Paper | ||||||
| Title of Event: | 25th IEEE International Conference on Image Processing (ICIP) | ||||||
| Type of Event: | Conference | ||||||
| Location of Event: | Athens, Greece | ||||||
| Date(s) of Event: | 7-10 Oct 2018 |
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