Online annotation of faces in personal videos by sequential learning

Date

2013-04-01

Author

Yilmazturk, M. C.
Ulusoy, İlkay
Çiçekli, Fehime Nihan

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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This paper addresses semi-automatic annotation of faces in personal videos. Different from previous offline annotation systems, this paper studies online annotation of faces. During an annotation session, few annotations are requested from the user only for some part of the video online. These annotations are used to train a system that will perform annotation automatically for the rest of the video. The automatic annotation results are presented to the user during the same session and the user is allowed to correct any automatic annotation mistakes. Thus, only fast and accurate face recognition methods are considered. Instead of batch learning, which has been used in the existing annotation systems, this paper proposes sequential learning methods to be used as face classifiers. Different classification methods are tested with various feature extraction methods using the same database so that a fair comparison is made among them. The results are evaluated in terms of recognition accuracies and execution time requirements.

Subject Keywords

Personal video annotation, Automatic annotation of faces, Sequential learning, RECOGNITION, RETRIEVAL

URI

https://hdl.handle.net/11511/37882

Collections

Department of Electrical and Electronics Engineering, Article