The nature of statistical learning theory
The nature of statistical learning theory
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information fusion in biometrics
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Guide to Biometrics
Personal Identification Based on Iris Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pupil and Iris Localization for Iris Recognition in Mobile Phones
SNPD-SAWN '06 Proceedings of the Seventh ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing
A New Iris Recognition Method Using Independent Component Analysis
IEICE - Transactions on Information and Systems
A robust eyelash detection based on iris focus assessment
Pattern Recognition Letters
A study on eyelid localization considering image focus for iris recognition
Pattern Recognition Letters
Iris feature extraction and matching based on multiscale and directional image representation
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Selection of optimal features for iris recognition
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Iris recognition in mobile phone based on adaptive gabor filter
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Iris recognition with support vector machines
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
A study on iris image restoration
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
A human identification technique using images of the iris andwavelet transform
IEEE Transactions on Signal Processing
IEEE Transactions on Circuits and Systems for Video Technology
A study on iris localization and recognition on mobile phones
EURASIP Journal on Advances in Signal Processing
A study on eyelid localization considering image focus for iris recognition
Pattern Recognition Letters
A novel iris segmentation using radial-suppression edge detection
Signal Processing
Robust iris verification based on local and global variations
EURASIP Journal on Advances in Signal Processing
Score level fusion of multimodal biometrics using triangular norms
Pattern Recognition Letters
Fake iris detection based on multiple wavelet filters and hierarchical SVM
ICISC'06 Proceedings of the 9th international conference on Information Security and Cryptology
New iris recognition method for noisy iris images
Pattern Recognition Letters
A review of information fusion techniques employed in iris recognition systems
International Journal of Advanced Intelligence Paradigms
Dimension reduction methods for image pattern recognition
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
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In conventional iris recognition methods, due to the difficulty of selecting one optimal wavelet filter for iris feature extraction, multiple wavelet filters (with different frequencies and kernel sizes) are adopted. However, this causes the processing time and the extracted feature size to increase. To overcome this problem, feature level fusion of the extracted iris features has been proposed, but this method requires a complicated dimension reduction procedure. Therefore, we propose a new iris recognition method based on score level fusion, using two Gabor wavelet filters and SVM (support vector machine). For score level fusion, we used the typical HD (Hamming distance) produced by a Gabor filter, which can easily be applied to conventional iris recognition systems. The proposed method has three novelties compared to previous works. First, when generating iris feature codes, we excluded detected eyelid, eyelash and SR (specular reflection) regions, which act as noise factors in iris feature extraction. Second, for enrollment, we checked the number of reliable iris feature codes that were not generated from the eyelid, eyelash and SR occluded regions. Only if the number of reliable codes exceeded in the predetermined threshold, we performed enrollment with high confidence, which reduced the FRR (false rejection rate). Third, two Gabor filters were used for local and global iris textures and the HDs calculated by those Gabor filters were fused by the SVM and the consequent authentication error was greatly reduced. Experimental results showed that the authentication error of the proposed method was much smaller than the authentication errors when using the single Gabor filter, the filter-bank, the score level and the decision level fusion methods.