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Face Recognition based on hybrid Contourlet features with Bayesian network

Published: 27 December 2017 Publication History

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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Do, M. N. and Vetterli, M. 2005. The contourlet transform: an efficient directional ultiresolution image representation. IEEE Transactions Image on Processing, 14, 12, (November. 2005), 2091--2106.
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cover image ACM Other conferences
ICVIP '17: Proceedings of the International Conference on Video and Image Processing
December 2017
272 pages
ISBN:9781450353830
DOI:10.1145/3177404
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • Nanyang Technological University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 December 2017

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Author Tags

  1. Bayesian network
  2. Contourlet transform
  3. Face recognition
  4. global and local features

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