A robust preprocessing algorithm for iris segmentation from low contrast eye images
Iris recognition systems offer highly accurate personal identification both on small and very large scale systems needed in government, forensic and commercial applications. The automatic segmentation of a noise free iris region is imperative for optimal performance of the system. However, image characteristics such as brightness and contrast, the differing levels of pigmentation, occlusion by eyelashes and/or eyelids, coupled with varying sensor and environmental conditions, makes iris segmentation a huge and difficult task. This paper proposes an image pre processing algorithm for robust iris segmentation of low contrast images, aimed at reducing mis localization errors of basic curve fitting algorithms. Similar to face detection, the algorithm performs iris detection with a k NN classifier trained with features extracted by a rotation invariant texture descriptor based on the co occurrence of local binary patterns. The integration of the proposed algorithm into an existing open source iris segmentation module offered a 40% improvement in execution time; a segmentation accuracy of 92% was also recorded over 1,898 low contrast eye images acquired from African subjects. The low contrast eye images were acquired to support diversity in iris recognition.
2016 Future Technologies Conference (FTC)