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Guang Huo received his PhD degree from the College of Computer Science and Technology, Jilin University, China, in 2016. He is an associate professor and supervisor of Master with Northeast Electric Power University. His research interests include pattern recognition, machine learning, biometrics, and image processing.
Huan Guo (March 13, 1994), Native place: Changchun Jilin (province), currently a first-year of postgraduate at the School of Computer Science and Technology, Northeast Electric Power University, China. Her research interests iris recognition.
Yangrui Zhang was born in Jilin, China in 1981. She received her M.A. degrees from the School of Foreign languages at Northeast Normal University in China in 2006. She is a senior lecturer with the School of Foreign languages at Northeast Electric Power University. Her research interests include linguistics, semantic analysis, and machine learning.
Qi Zhang (April 15, 1992), Native place: Changchun Jilin (province), currently a second-year of postgraduate at the School of Computer Science and Technology, Northeast Electric Power University, China. Her research interests iris recognition.
Wenyu Li (July 7, 1994), Native place: Anshan Liaoning (province), currently a first-year of postgraduate at the School of Computer Science and Technology, Northeast Electric Power University, China. Her research interests iris recognition.
Bin Li was born in Changchun, Jilin, China in 1982. He received his M.S. and PhD degrees from the School of Computer Science and Technology at Jilin University in China in 2011 and 2015, respectively. He is currently an Associate Professor with the School of Computer Science at Northeast Electric Power University. His research interests include image processing, computer vision, and pattern recognition.
Iris recognition is recognized as one of the most reliable and efficient technique for human identification in the biometric fields. The Gabor filter and local binary pattern (LBP) are widely adopted for feature extraction in face recognition. However, it is difficult to achieve high recognition accuracy when the Gabor filter or LBP is directly applied to iris texture representation. This paper presents an effective iris feature descriptor, which first uses 2D-Gabor filter to extract multi-orientation imaginary (MOI) feature, and then applies uniform LBP for region feature encoding. Thus, the MOI feature-by-point energy is converted into that of the uniform LBP histogram-by-block, during which the distributions of the intra- and inter-class are greatly widened. Such process largely improves distinguishability of MOI features. Finally, the Bhattacharyya distance is adopted for matching. Experimental results on CASIA and JLU iris image databases show that this method performs better for combining MOI features and LBP encoding as compared to their individual function.
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- An Effective Feature Descriptor with Gabor Filter and Uniform Local Binary Pattern Transcoding for Iris Recognition
- Pleiades Publishing
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