Thai H. Le

Work place: Computer Science Department, VNUHCM - University of Science, 70000, VietNam



Research Interests: Computer systems and computational processes, Computer Vision, Pattern Recognition, Image Compression, Image Manipulation, Image Processing


Prof. Dr. Thai H. Le received B.S degree and M.S degree in Computer Science from Hanoi University of Technology, Vietnam, in 1995 and 1997. He received Ph.D. degree in Computer Science from VNUHCM - University of Science, Vietnam, in 2004. Since 1999, he has been a lecturer at Faculty of Information Technology, VNUHCM - University of Science, Vietnam. His research interests include soft computing pattern recognition, image processing, biometric and computer vision. Prof. Dr. Le Hoang Thai is the co-author of many published papers of international journals and international conferences.

Author Articles
Person Authentication using Relevance Vector Machine (RVM) for Face and Fingerprint

By Long B. Tran Thai H. Le

DOI:, Pub. Date: 8 May 2015

Multimodal biometric systems have proven more efficient in personal verification or identification than single biometric ones, so it is also a focus of this paper. Particularly, in the paper, the authors present a multimodal biometric system in which features from face and fingerprint images are extracted using Zernike Moment (ZM), the personal authentication is done using Relevance Vector Machine (RVM) and feature-level fusion technique. The proposed system has proven its remarkable ability to overcome the limitations of uni-modal biometric systems and to tolerate local variations in the face or fingerprint image of an individual. Also, the achieved experimental results have demonstrated that using RVM can assure a higher level of forge resistance and enables faster authentication than the state-of-the-art technique , namely the support vector machine (SVM).

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Using Wavelet-Based Contourlet Transform Illumination Normalization for Face Recognition

By Long B. Tran Thai H. Le

DOI:, Pub. Date: 8 Jan. 2015

Evidently, the results of a face recognition system can be influenced by image illumination conditions. Regarding this, the authors proposed a system using wavelet-based contourlet transform normalization as an efficient method to enhance the lighting conditions of a face image. Particularly, this method can sharpen a face image and enhance its contrast simultaneously in the frequency domain to facilitate the recognition. The achieved results in face recognition tasks experimentally performed on Yale Face Database B have demonstrated that face recognition system with wavelet-based contourlet transform can perform better than any other systems using histogram equalization for its efficiency under varying illumination conditions.

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