Image understanding for iris biometrics: A survey

By Kevin W. Bowyer, Karen Hollingsworth, Patrick J. Flynn. Computer Vision and Image Understanding, 2008

1,034 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Genetics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/da51f1c1db6a5b1f78eb2c518a13e3e9128850e3

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Anil K. Jain
2. Vishal M. Patel
3. Ramalingam Chellappa
4. Arun A. Ross
5. Luc Van Gool
6. Dacheng Tao
7. Xiaogang Wang
8. Zhenan Sun

In Genetics

1. Zhaofeng He
2. Zhenan Sun
3. Tien-Ping Tan
4. Almas Abbasi
5. Woo Chaw Seng
6. J. Unar
7. Richa Singh
8. Mayank Vatsa

In Signal Processing

1. Anil K. Jain
2. Arun A. Ross
3. Vishal M. Patel
4. Ramalingam Chellappa
5. Zhenan Sun
6. Tien-Ping Tan
7. Richa Singh
8. Mayank Vatsa

Citations in the corpus

• 50 Years of object recognition: Directions forward (2013, background): “Biometric vision-based systems for fingerprint recognition [17], iris pattern recognition [18], as well as finger-vein and palm-vein patterns [19, 20] have also gained acceptance by the law enforcement community and are widely used.”
• Secure and Robust Iris Recognition Using Random Projections and Sparse Representations (2011, methodology): “See [1] for an excellent survey of recent efforts on iris recognition.”
• Toward Accurate and Fast Iris Segmentation for Iris Biometrics (2009, neutral): “ITH the increasing demands of security in our daily life, iris recognition has rapidly become a hot research topic for its potential application values in personal identification [1], [3], [8], [9], [12].”
• Iris Segmentation Using Geodesic Active Contours (2009, background): “Large-scale authentication experiments have confirmed this notion further underscoring the relevance of this biometric trait in distinguishing individuals [2], [3].”
• FRVT 2006 and ICE 2006 large-scale results (2007, neutral): “The identification of any commercial product or trade name does not imply endorsement or recommendation by the National Institute of Standards and Technology, SAIC, Schafer Corp., U. of Texas at Dallas or U. of Notre Dame.”
• Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing (2008, background): “A detailed literature survey of iris recognition algorithms can be found in [28].”
• The UBIRIS.v2: A Database of Visible Wavelength Iris Images Captured On-the-Move and At-a-Distance (2010, background): “As an illustration, over 50 percent of the publications cited in a recent iris recognition survey [3] were published since 2005.”
• Periocular Biometrics in the Visible Spectrum (2011, background): “Both technological and operational tests conducted under predominantly constrained conditions have demonstrated the uniqueness of the iris texture to an individual and its potential as a biometric in large-scale systems enrolling millions of individuals [7], [9].”
• Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition (2010, background): “Many researchers have contributed much in iris segmentation, and encouraging performance has been achieved [1,3,14,15].”
• A review of biometric technology along with trends and prospects (2014, methodology): “An automated iris recognition system uses the unique and stable texture consisting of crypts, furrows, corona and freckles of human iris [144, 145].”