A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Classification by Wavelet Packet Signatures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Muliscale Vessel Enhancement Filtering
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Personal Identification Based on Iris Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast Parallel Thinning Algorithm for the Binary Image Skeletonization
International Journal of High Performance Computing Applications
Multispectral Iris Analysis: A Preliminary Study51
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Minutiae feature analysis for infrared hand vein pattern biometrics
Pattern Recognition
A novel 3D multi-scale lineness filter for vessel detection
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
Fake iris detection by using purkinje image
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Texture analysis and classification with tree-structured wavelet transform
IEEE Transactions on Image Processing
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
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Liveness detection is a necessary step towards higher reliability of iris recognition. In this research, we propose a novel iris liveness detection method based on multi-features extracted from multispectral images. First, we analyze the specific multispectral characteristics of conjunctival vessels and iris textures. To ensure the effective utilization of these characteristics, iris images are simultaneously captured at near-infrared (860nm) and blue (480nm) wavelengths. Then we respectively define and measure relative number of conjunctival vessels (RNCV) and entropy ratio of iris textures (ERIT) using 860-nm and 480-nm images. Finally, the feature values of RNCV and ERIT are arranged to form a robust 2-D feature vector. The trained Support Vector Machine (SVM) is used to classify the feature vectors extracted from live and fake irises. Experimental results demonstrate that the proposed method can discriminate between live irises and various types of fake irises with high classification accuracy and low computational cost.