Robust automatic face clustering in news video

Anantharajah, Kaneswaran, Denman, Simon, Tjondronegoro, Dian, Sridharan, Sridha, & Fookes, Clinton (2015) Robust automatic face clustering in news video. In 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE, Adelaide Town Hall, Adelaide, South Australia, pp. 1-8.

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Clustering identities in a video is a useful task to aid in video search, annotation and retrieval, and cast identification. However, reliably clustering faces across multiple videos is challenging task due to variations in the appearance of the faces, as videos are captured in an uncontrolled environment. A person's appearance may vary due to session variations including: lighting and background changes, occlusions, changes in expression and make up.

In this paper we propose the novel Local Total Variability Modelling (Local TVM) approach to cluster faces across a news video corpus; and incorporate this into a novel two stage video clustering system. We first cluster faces within a single video using colour, spatial and temporal cues; after which we use face track modelling and hierarchical agglomerative clustering to cluster faces across the entire corpus. We compare different face recognition approaches within this framework. Experiments on a news video database show that the Local TVM technique is able effectively model the session variation observed in the data, resulting in improved clustering performance, with much greater computational efficiency than other methods.

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ID Code: 92832
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Face clustering, Total Variability Modelling
DOI: 10.1109/DICTA.2015.7371301
ISBN: 9781467367950
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Australian and New Zealand Standard Research Classification
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Facilities: Science and Engineering Centre
Copyright Owner: Copyright 2015 IEEE
Copyright Statement: 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.
Deposited On: 12 Feb 2016 02:06
Last Modified: 15 Feb 2016 18:09

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