Abstract
In this paper we introduce a novel human face identification scheme from video data based on a frequency domain representation of facial asymmetry. A Hidden Markov Model (HMM) is used to learn the temporal dynamics of the training video sequences of each subject and classification of the test video sequences is performed using the likelihood scores obtained from the HMMs. We apply this method to a video database containing 55 subjects showing extreme expression variations and demonstrate that the HMM-based method performs much better than identification based on the still images using an Individual PCA (IPCA) classifier, achieving more than 30% improvement.
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Mitra, S., Savvides, M., Vijaya Kumar, B.V.K. (2006). Human Face Identification from Video Based on Frequency Domain Asymmetry Representation Using Hidden Markov Models. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds) Multimedia Content Representation, Classification and Security. MRCS 2006. Lecture Notes in Computer Science, vol 4105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11848035_5
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DOI: https://doi.org/10.1007/11848035_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-39392-4
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