Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Iris Recognition Algorithm Using Modified Log-Gabor Filters
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
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
Handbook of Biometrics
Image understanding for iris biometrics: A survey
Computer Vision and Image Understanding
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
An efficient iris recognition using local feature descriptor
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Real-time iris segmentation based on image morphology
Proceedings of the 2011 International Conference on Communication, Computing & Security
New Methods in Iris Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Efficient iris recognition by characterizing key local variations
IEEE Transactions on Image Processing
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This article looks into pros and cons of the conventional global and local feature matching techniques for iris. The review of related research works on matching techniques leads to the observation that local features like scale invariant feature transform SIFT gives satisfactory recognition accuracy for good quality images. However the performance degrades when the images are occluded or taken non-cooperatively. As SIFT matches keypoints on the basis of 128-D local descriptors, hence it sometimes falsely pairs two keypoints which are from different portions of two iris images. Subsequently the need for filtering or pruning of faulty SIFT pairs is felt. The paper proposes two methods of filtering impairments faulty pairs based on the knowledge of spatial information of the keypoints. The two proposed pruning algorithms angular filtering and scale filtering are applied separately and applied in union to have a complete comparative analysis of the result of matching. The pruning approaches has given better recognition accuracy than conventional SIFT when experimented on two publicly available BATH and CASIAv3 iris databases.