High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
On the Use of SIFT Features for Face Authentication
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
A human identification technique using images of the iris andwavelet transform
IEEE Transactions on Signal Processing
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This paper proposes an iris recognition system which can handle efficiently the problem of rotation, scaling, change in gaze of individual and partial occlusions that are inherent to non-restrictive iris imaging system. In addition to this, traditional iris normalisation approach deforms texture features linearly due to change in camera to eye distance or non-uniform illumination. To overcome the effect of aliasing features are extracted directly from annular region of iris using Speeded Up Robust Features (SURF). These features are invariant to transformations and occlusion. The system is tested on BATH, CASIA and IITK databases and is showing an accuracy of more than 97%. From the results it is inferred that local features from annular iris gives much better accuracy for poor quality images in comparison to normalised iris.