High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Noncooperative Iris Recognition: A Classification Approach Using Multiple Signatures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image understanding for iris biometrics: A survey
Computer Vision and Image Understanding
An Effective Approach for Iris Recognition Using Phase-Based Image Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ordinal Measures for Iris Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust and accurate iris segmentation in very noisy iris images
Image and Vision Computing
The UBIRIS.v2: A Database of Visible Wavelength Iris Images Captured On-the-Move and At-a-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
UBIRIS: a noisy iris image database
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
A human identification technique using images of the iris andwavelet transform
IEEE Transactions on Signal Processing
Combining SIFT and global features for web image classification
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
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In iris recognition systems how to represent texture pattern is an important issue. The paper proposes a novel approach based on SIFT for feature representation of iris texture. This approach partitions a normalized iris image into non-overlapping small sub-images and uses SIFT descriptor for representing the characteristics of each sub-image. As such the iris texture pattern is represented by an ordered-set of SIFT descriptors. This representation is very distinctive and insensitive to illumination changes. In addition, it encodes the positional information of iris texture pattern. For iris matching we use Bhattacharyya distance to measure the dissimilarity between two SIFT descriptors. The final distance is a sum of the distances of the corresponding pairs of SIFT descriptors in two iris images. The experimental results on UBIRIS.v1 and UBIRIS.v2 show that proposed method has promising performance.