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Adaptive weighted local textural features for illumination, expression and occlusion invariant face recognition

Cui, Chen
http://orcid.org/0000-0003-1622-8010
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1374782158

Abstract Details

2013, Master of Science (M.S.), University of Dayton, Electrical Engineering.
Face recognition is one of the most promising biometric methodologies for human identification in a non-cooperative security environment. Several algorithms have been developed for extracting different facial features for face recognition. Due to the various possible challenges of data captured at different lighting conditions, viewing angles, facial expressions, and partial occlusions in natural environmental conditions, automatic facial recognition is still remaining as a difficult issue that needs to be resolved. In this thesis, we propose a novel approach to tackle some of these issues by analyzing the local textural descriptions for facial feature representation. The textural information is extracted by an enhanced local binary pattern description of all the local regions in the face, and the dimensionality of the texture image is reduced by principal component analysis performed on each local face region independently. The face feature vector is obtained by concatenating the reduced dimensional weight set of each module (sub-region) of the face image. The weightage of each sub-region is determined by employing the local variance estimate of the respective region which represents the significance of the region. Experiments conducted on various popular face databases show promising performance of the proposed algorithm in varying lighting, expression, and partial occlusion conditions. Research work is progressing to investigate the effectiveness of the proposed face recognition method on pose varying conditions as well. It is envisaged that a multilane approach of trained frameworks at different pose bins and an appropriate voting strategy would lead to a good recognition rate in such situation.
Vijayan Asari, Ph.D. (Committee Chair)
Balster Eric, Ph.D. (Committee Member)
Ordóñez Raúl, Ph.D. (Committee Member)
Electrical Engineering
Enhanced Local Binary Pattern; Weighted Modular Principal Component Analysis; Face Recognition; Feature Extraction

Recommended Citations

Citations

  • Cui, C. (2013). Adaptive weighted local textural features for illumination, expression and occlusion invariant face recognition [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1374782158

    APA Style (7th edition)

  • Cui, Chen. Adaptive weighted local textural features for illumination, expression and occlusion invariant face recognition. 2013. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1374782158.

    MLA Style (8th edition)

  • Cui, Chen. "Adaptive weighted local textural features for illumination, expression and occlusion invariant face recognition." Master's thesis, University of Dayton, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1374782158

    Chicago Manual of Style (17th edition)

dayton1374782158
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© 2013, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.