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Recognizing frontal face images using hidden Markov models with one training image per person
2004 (English)In: Proceedings of the 17th international conference on pattern recognition, vol 1 / [ed] Kittler, J; Petrou, M; Nixon, M, Los Alamitos: IEEE Computer Society, 2004, 318-321 p.Conference paper (Refereed)Text
Abstract [en]

Recently, many important face recognition systems could deal well with frontal view face images. However few of them work well when there is only one training image per person. In this paper we propose an approach to cope with the problem by using ID Discrete Hidden Markov Model (ID-DHMM). The model training and recognition part were carried out on both vertical and horizontal directions. New way of extracting observations and using observation sequences in recognition is introduced. The Haar wavelet transform was applied to the image to lessen the dimension of the observation vectors. Our experiment results tested on the frontal view AR Face Database show that the proposed method outperforms the PCA, LDA, LFA approaches tested on the same database.

Place, publisher, year, edition, pages
Los Alamitos: IEEE Computer Society, 2004. 318-321 p.
, International conference on pattern recognition, ISSN 1051-4651
National Category
Computer Science
URN: urn:nbn:se:umu:diva-122194DOI: 10.1109/ICPR.2004.1334116ISI: 000223874500078ISBN: 0-7695-2128-2OAI: diva2:938079
17th International Conference on Pattern Recognition (ICPR), AUG 23-26, 2004, British Machine Vis Assoc, Cambridge, ENGLAND
Available from: 2016-06-16 Created: 2016-06-15 Last updated: 2016-06-16Bibliographically approved

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