@inproceedings{bc3eb98d343941cfa1720e5b3c4266dc,
title = "Dual-feature Bayesian MAP classification: Exploiting temporal information for video-based face recognition",
abstract = "Machine recognition of faces in video is an emerging problem. Following recent advances, conventional exemplar-based schemes and image set approaches inadequately exploit temporal information in video sequences for the classification task. In this work, we propose a new dual-feature Bayesian maximum-a-posteriori (MAP) classification method for face recognition in video sequences. Both cluster and exemplar features are extracted and unified under a compact probabilistic framework. To realize a non-parametric solution, a joint probability function is modeled using relevant similarity measures for matching these features. Extensive experiments on two public face video datasets demonstrate the good performance of our proposed method.",
keywords = "Bayesian MAP classification, feature fusion, similarity measures, video-based face recognition",
author = "John See and Chikkannan Eswaran and Fauzi, {Mohammad Faizal Ahmad}",
year = "2012",
doi = "10.1007/978-3-642-34500-5_65",
language = "English",
isbn = "9783642344992",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "549--556",
editor = "T. Huang and Z. Zeng and C. Li and C.S. Leung",
booktitle = "Neural Information Processing. ICONIP 2012",
note = "19th International Conference on Neural Information Processing 2012, ICONIP 2012 ; Conference date: 12-11-2012 Through 15-11-2012",
}