Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Personal Identification Based on Iris Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Accurate and Fast Iris Segmentation for Iris Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph matching iris image blocks with local binary pattern
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Fake iris detection by using purkinje image
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
Statistical texture analysis-based approach for fake iris detection using support vector machines
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Adaboost and multi-orientation 2D Gabor-based noisy iris recognition
Pattern Recognition Letters
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Recently, spoof detection has become an important and challenging topic in iris recognition. Based on the textural differences between the counterfeit iris images and the live iris images, we propose an efficient method to tackle this problem. Firstly, the normalized iris image is divided into sub-regions according to the properties of iris textures. Local binary patterns (LBP) are then adopted for texture representation of each sub-region. Finally, Adaboost learning is performed to select the most discriminative LBP features for spoof detection. In particular, a kernel density estimation scheme is proposed to complement the insufficiency of counterfeit iris images during Adaboost training. The comparison experiments indicate that the proposed method outperforms state-of-the-art methods in both accuracy and speed.