Comparison of eyelid and eyelash detection algorithms for performance improvement of iris recognition

Eyelids and eyelashes occluding the iris region are noise factors that degrade the performance of iris recognition. If they are incorrectly classified as the iris region, the false iris pattern information will increase, decreasing the recognition rate. Thus, reliable detection of eyelids and eyelashes is required to improve the performance of iris recognition. The objective of this paper is to present a research direction for reduction of noise factors based on analysis and performance comparison of existing eyelid and eyelash detection algorithms. With CASIA version 3 database we compare six existing eyelid and eyelash detection algorithms and analyze the characteristics and performance of each algorithm. The performance of each algorithm is evaluated in terms of different performance measures such as the decidability, the equal error rate, and the detection error trade-off curve.