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Local intensity area descriptor for facial recognition in ideal and noise conditions

[+] Author Affiliations
Chi-Kien Tran

National Kaohsiung University of Applied Sciences, Medical Physics and Informatics Laboratory of Electronics Engineering, Kaohsiung, Taiwan

Hanoi University of Industry, Centre for Information Technology, Hanoi, Vietnam

Chin-Dar Tseng

National Kaohsiung University of Applied Sciences, Medical Physics and Informatics Laboratory of Electronics Engineering, Kaohsiung, Taiwan

Pei-Ju Chao

National Kaohsiung University of Applied Sciences, Medical Physics and Informatics Laboratory of Electronics Engineering, Kaohsiung, Taiwan

Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Department of Radiation Oncology, Kaohsiung, Taiwan

Hui-Min Ting, Yu-Jie Huang

Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Department of Radiation Oncology, Kaohsiung, Taiwan

Liyun Chang

I-Shou University, Department of Medical Imaging and Radiological Sciences, Kaohsiung, Taiwan

Tsair-Fwu Lee

National Kaohsiung University of Applied Sciences, Medical Physics and Informatics Laboratory of Electronics Engineering, Kaohsiung, Taiwan

Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Department of Radiation Oncology, Kaohsiung, Taiwan

Kaohsiung Medical University, Institute of Clinical Medicine, Taiwan

J. Electron. Imaging. 26(2), 023011 (Mar 25, 2017). doi:10.1117/1.JEI.26.2.023011
History: Received November 8, 2016; Accepted March 14, 2017
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Abstract.  We propose a local texture descriptor, local intensity area descriptor (LIAD), which is applied for human facial recognition in ideal and noisy conditions. Each facial image is divided into small regions from which LIAD histograms are extracted and concatenated into a single feature vector to represent the facial image. The recognition is performed using a nearest neighbor classifier with histogram intersection and chi-square statistics as dissimilarity measures. Experiments were conducted with LIAD using the ORL database of faces (Olivetti Research Laboratory, Cambridge), the Face94 face database, the Georgia Tech face database, and the FERET database. The results demonstrated the improvement in accuracy of our proposed descriptor compared to conventional descriptors [local binary pattern (LBP), uniform LBP, local ternary pattern, histogram of oriented gradients, and local directional pattern]. Moreover, the proposed descriptor was less sensitive to noise and had low histogram dimensionality. Thus, it is expected to be a powerful texture descriptor that can be used for various computer vision problems.

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Citation

Chi-Kien Tran ; Chin-Dar Tseng ; Pei-Ju Chao ; Hui-Min Ting ; Liyun Chang, et al.
"Local intensity area descriptor for facial recognition in ideal and noise conditions", J. Electron. Imaging. 26(2), 023011 (Mar 25, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.2.023011


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Longitudinal Study of Automatic Face Recognition. IEEE Trans Pattern Anal Mach Intell Published online Jan 16, 2017;
Collaborative Random Faces-Guided Encoders for Pose-Invariant Face Representation Learning. IEEE Trans Neural Netw Learn Syst Published online Feb 01, 2017;
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