An approach of context ontology for robust face recognition against illumination variations
Paper
Paper/Presentation Title | An approach of context ontology for robust face recognition against illumination variations |
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Presentation Type | Paper |
Authors | Bashar, M. Rezaul (Author), Li, Yan (Author) and Rhee, Phill Kyu (Author) |
Editors | Kabir, Lutful and Hasan, Kamrul |
Journal or Proceedings Title | Proceedings of the International Conference on Information and Communication Technology (ICICT 2007) |
Number of Pages | 5 |
Year | 2007 |
Place of Publication | Dhaka, Bangladesh |
ISBN | 9843233948 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ICICT.2007.375351 |
Web Address (URL) of Paper | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4261374 |
Conference/Event | ICICT 2007: International Conference on Information and Communication Technology |
Event Details | ICICT 2007: International Conference on Information and Communication Technology Event Date 07 to end of 09 Mar 2007 Event Location Dhaka, Bangladesh |
Abstract | This paper proposes a face recognition method that is robust against image variations due to arbitrary lighting condition. Though many researches have been carried out on face recognition system, however; there exist some limitations such as illumination, pose, alignment, occlusion, etc. This paper presents a context ontology model making a robust face recognition system on different illumination situations. Our proposed system works on two phases: environmental context ontology building (modelling) and recognition using context ontology. Context ontology is built using data acquisition, context learning and context categorization. The recognition approach is implemented on illumination variant face recognition that takes identified context as input and performs recognition with usual process such as pre-processing, feature extraction, learning, and recognition. We have tested the recognition performance of our proposed model with an international standard FERET face database (our produced synthesized FERET images) and we have achieved a success rate of more than 92%. |
Keywords | context ontology; feature extraction; face recognition |
ANZSRC Field of Research 2020 | 460306. Image processing |
400607. Signal processing | |
469999. Other information and computing sciences not elsewhere classified | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Islamic University, Bangladesh |
Department of Mathematics and Computing | |
Inha University, Korea |
https://research.usq.edu.au/item/9y4xy/an-approach-of-context-ontology-for-robust-face-recognition-against-illumination-variations
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