Abstract : The object of interest of this paper is Automatic Face Recognition (AFR). The usual methods need a labeled corpus and the number of training examples plays a crucial role for the recognition accuracy. Unfortunately, the corpus creation is very expensive and time consuming task. Therefore, the motivation of this work is to propose and implement new AFR approaches that could solve this issue and perform well also with few training examples. Our approaches extend the successful method based on the Scale Invariant Feature Transform (SIFT) proposed by Aly. We propose and evaluate two methods: the Lenc-Kral matching and the SIFT based Kepenekci approach [7]. Our approaches are evaluated on two face data-sets: the ORL database and the Czech News Agency (ČTK) corpus. We experimentally show that the proposed approaches significantly outperform the baseline Aly method on both corpora.
Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-381 (Part I), pp.254-263, 2012, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-33409-2_27〉
https://hal.inria.fr/hal-01521428
Contributeur : Hal Ifip
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Soumis le : jeudi 11 mai 2017 - 17:10:44
Dernière modification le : vendredi 1 décembre 2017 - 01:16:30
Document(s) archivé(s) le : samedi 12 août 2017 - 14:03:48
Ladislav Lenc, Pavel Kral. Novel Matching Methods for Automatic Face Recognition Using SIFT. Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-381 (Part I), pp.254-263, 2012, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-33409-2_27〉. 〈hal-01521428〉
