High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving iris recognition accuracy via cascaded classifiers
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Efficient iris recognition by characterizing key local variations
IEEE Transactions on Image Processing
Image understanding for iris biometrics: A survey
Computer Vision and Image Understanding
Optimal features subset selection and classification for iris recognition
Journal on Image and Video Processing - Regular
Efficient Iris Spoof Detection via Boosted Local Binary Patterns
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Using fragile bit coincidence to improve iris recognition
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Personal identification using periocular skin texture
Proceedings of the 2010 ACM Symposium on Applied Computing
Iris recognition by fusing different representations of multi-scale Taylor expansion
Computer Vision and Image Understanding
A comparison of genetic feature selection and weighting techniques for multi-biometric recognition
Proceedings of the 49th Annual Southeast Regional Conference
An appearance-based approach to assistive identity inference using LBP and colour histograms
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
On the commonality of iris biometrics
Proceedings of the 9th International Conference on Advances in Mobile Computing and Multimedia
Noisy Iris Recognition Integrated Scheme
Pattern Recognition Letters
A reliable iris recognition algorithm based on reverse biorthogonal wavelet transform
Pattern Recognition Letters
Palmprint recognition based on directional features and graph matching
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
A review of information fusion techniques employed in iris recognition systems
International Journal of Advanced Intelligence Paradigms
Hi-index | 0.00 |
Iris-based personal identification has attracted much attention in recent years. Almost all the state-of-the-art iris recognition algorithms are based on statistical classifier and local image features, which are noise sensitive and hardly to deliver perfect recognition performance. In this paper, we propose a novel iris recognition method, using the histogram of local binary pattern for global iris texture representation and graph matching for structural classification. The objective of our idea is to complement the state-of-the-art methods with orthogonal features and classifier. In the texture-rich iris image database UPOL, our method achieves higher discriminability than state-of-the-art approaches. But our algorithm does not perform well in the CASIA database whose images are less textured. Then the value of our work is demonstrated by providing complementary information to the state-of-the-art iris recognition systems. After simple fusion with our method, the equal error rate of Daugman’s algorithm could be halved.