@inbook{ed44e2cc2b3b4df6be516f6e0c920950,
title = "An iris recognition algorithm using local extreme points",
abstract = "The performance of an iris recognition algorithm depends greatly on its classification ability as well as speed. In this paper, an iris recognition algorithm using local extreme points is proposed. It first detects the local extreme points along the angular direction as key points. Then, the sample vector along the angular direction is encoded into a binary feature vector according to the surface trend (gradient) characterized by the local extreme points. Finally, the Hamming distance between two iris patterns is calculated to make a decision. Extensive experimental results show the high performance of the proposed method in terms of accuracy and speed.",
author = "Jiali Cui and Yunhong Wang and Tieniu Tan and Li Ma and Zhenan Sun",
year = "2004",
doi = "10.1007/978-3-540-25948-0\_61",
language = "英语",
isbn = "3540221468",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "442--449",
editor = "David Zhang and Jain, \{Anil K.\}",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "德国",
}