Biometric personal identification based on iris patterns

By Yong Zhu, Tien-Ping Tan, Yunhong Wang. Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2000

427 citations recorded by the source corpus.

Subfields: Signal Processing

View Paper: https://www.semanticscholar.org/paper/c281747484c2e777cd61569a172d41a0128afd91

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Signal Processing

1. Tien-Ping Tan
2. Richa Singh
3. Mayank Vatsa
4. Afzel Noore
5. Dexin Zhang
6. Anil K. Jain
7. Yunhong Wang
8. Li Ma

Citations in the corpus

• Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing (2008, background): “Wildes [5], Boles [6], and several other researchers proposed different recognition algorithms [7]-[32].”
• Iris recognition using circular symmetric filters (2002, methodology): “Our previous methods [12][13] use Multichannel Gabor filters to extract both local and global details in an iris.”
• Combining Face and Iris Biometrics for Identity Verification (2003, background): “There are four main approaches to iris representation: phase information coding [13][16], zero-crossings representation [24], shape description [25] and texture analysis [14-15][25-28].”
• Personal Identification Based on Iris Texture Analysis (2003, background): “3, the second image is out of focus, the third one contains many noticeable interlacing lines (especially in regions close to the boundary) caused by eye motion, and the last one is an example of severe occlusions by eyelids and eyelashes.”
• Iris Recognition Based on Multichannel Gabor Filtering (2002, methodology): “Our early work on iris recognition was described in [15].”
• Recognition of Human Iris Patterns for Biometric Identification (2003, neutral): “[11] and is specified as”
• Efficient iris recognition by characterizing key local variations (2004, methodology): “Our earlier attempts to iris recognition developed the texture analysis-based methods [14], [17], [18] and a local intensity variation analysis-based method [37].”