Face Recognition based on hybrid Contourlet features with Bayesian network
Pages 73 - 77
Abstract
In this paper, we used hybrid features together the Bayesian network for face recognition. Hybrid features are based on Contourlet coefficients in local regions and the whole face. The combination of global features and local features achieved high accuracy. For local features, we use properties of the regions such as eyes, mouth. For global, we use the features of the whole face. The properties are converted to Contourlet domain to keep directions as well as important key points. Bayesian Network based on graph theory and probability, provide a natural tool for two problems: uncertainty and complexity and flexible, fit more datasets. The experimental results on the Olivetti Research Laboratory (ORL) [1] face dataset show that the proposed method can effectively and greatly increase the recognition.
References
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The ORL Database of faces: http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
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Index Terms
- Face Recognition based on hybrid Contourlet features with Bayesian network
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Published In
December 2017
272 pages
ISBN:9781450353830
DOI:10.1145/3177404
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- Nanyang Technological University
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Association for Computing Machinery
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Published: 27 December 2017
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ICVIP 2017
ICVIP 2017: International Conference on Video and Image Processing
December 27 - 29, 2017
Singapore, Singapore
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