Feature Extraction of Iris Based on Texture Analysis

He, Yufeng; Ma, Zheng; Zhang, Yun

doi:10.1007/978-3-642-29387-0_83

Cited by 4 publications

(1 citation statement)

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“…Fusion of local distances was used as an identification approach. He et al [18] focused on the quality assessment issue and proposed a new scheme for iris preprocessing. K-means clustering was used to classify the blurred, defocused and occluded image from clear iris.…”

Section: Related Workmentioning

confidence: 99%

Efficient enhancement and matching for iris recognition using SURF

Ismail

Ali

Farag

2015

2015 5th National Symposium on Information Technology: Towards New Smart World (NSITNSW)

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Iris recognition is gaining more attention and the development of the field is increasing rapidly. This paper presents a complete iris recognition system. The iris features are obtained using Speeded Up Robust Features (SURF) after enhancing the image using Contrast Limited Adaptive Histogram Equalization (CLAHE). A novel matching algorithm based on applying fusion rules at different levels is proposed. The algorithm has the advantage of reduced data storage and fast matching. It can also handle efficiently the problem of rotation, scaling, illumination variation and occlusions. The proposed algorithm is implemented and tested using CASIA (V4) database. The recognition accuracies obtained are 99% using left images and 99.5% using right images. Results show that fusion of right and left images scores increases the recognition accuracy. The recognition accuracies obtained after fusion are 99.5% and 100% using minimum and sum rules respectively. Moreover, the proposed algorithm has an excellent robustness with respect to increasing the number of subjects.

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Section: Related Workmentioning

confidence: 99%