Probabilistic Matching of Image Sets for Video-Based Face Recognition
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Tjondronegoro, Dian
Chandran, Vinod
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Fremantle, AUSTRALIA
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Abstract
We address the problem of face recognition on video by employing the recently proposed probabilistic linear discrimi-nant analysis (PLDA). The PLDA has been shown to be robust against pose and expression in image-based face recognition. In this research, the method is extended and applied to video where image set to image set matching is performed. We investigate two approaches of computing similarities between image sets using the PLDA: the closest pair approach and the holistic sets approach. To better model face appearances in video, we also propose the heteroscedastic version of the PLDA which learns the within-class covariance of each individual separately. Our experi-ments on the VidTIMIT and Honda datasets show that the combination of the heteroscedastic PLDA and the closest pair approach achieves the best performance.
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2012 International Conference on Digital Image Computing Techniques and Applications (DICTA)
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© 2012 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.
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Science & Technology
Imaging Science & Photographic Technology
video-based face recognition
image set matching
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Wibowo, ME; Tjondronegoro, D; Chandran, V, Probabilistic Matching of Image Sets for Video-Based Face Recognition, 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA), 2012