Specific Texture Analysis for Iris Recognition

Krichen, Emine; Allano, Lorène; Garcia-Salicetti, Sonia; Dorizzi, Bernadette

doi:10.1007/11527923_3

Cited by 19 publications

(14 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ma et al [19] also enrolled three iris images and averaged the three Hamming distances to obtain the final score. Krischen et al [20] used the minimum of the three Hamming distance as the final score. Schmid et al [21] demonstrated that fusing the scores using log likelihood ratio gave superior performance when compared to average Hamming distance.…”

Section: B Iris Recognition From Videosmentioning

confidence: 99%

Secure and Robust Iris Recognition Using Random Projections and Sparse Representations

Pillai

Patel

Chellappa

et al. 2011

IEEE Trans. Pattern Anal. Mach. Intell.

293

155

View full text Add to dashboard Cite

Abstract-Non-contact biometrics such as face and iris have additional benefits over contact based biometrics such as fingerprint and hand geometry. However, three important challenges need to be addressed in a non-contact biometrics-based authentication system: ability to handle unconstrained acquisition, robust and accurate matching and privacy enhancement without compromising security. In this paper, we propose a unified framework based on random projections and sparse representations, that can simultaneously address all the three issues mentioned above in relation to iris biometrics. Our proposed quality measure can handle segmentation errors and a wide variety of possible artifacts during iris acquisition. We demonstrate how the proposed approach can be easily extended to handle alignment variations and recognition from iris videos, resulting in a robust and accurate system. The proposed approach includes enhancements to privacy and security by providing ways to create cancelable iris templates. Results on public datasets show significant benefits of the proposed approach.

show abstract

Section: B Iris Recognition From Videosmentioning

confidence: 99%

Secure and Robust Iris Recognition Using Random Projections and Sparse Representations

Pillai

Patel

Chellappa

et al. 2011

IEEE Trans. Pattern Anal. Mach. Intell.

293

155

View full text Add to dashboard Cite

show abstract

“…Ma et al [4] also used three templates of a given iris in their enrollment database, and took the average of three scores as the final matching score. Krichen et al [5] performed a similar experiment, but used the minimum match score instead of the average. Schmid et al [6] present two methods for fusing Hamming distance scores.…”

Section: Related Workmentioning

confidence: 99%

“…Iris recognition is no exception. Many researchers [4,5,6] enroll multiple images, obtain multiple Hamming distance scores, and then fuse the scores together to make a decision. However, the different researchers have chosen different ways to combine the information from multiple Hamming distance scores.…”

Section: Comparison To Previous Workmentioning

confidence: 99%

“…We will call this type of experiment an N-to-1-average comparison. Krichen et al [5] also enrolled N gallery images of a particular subject. However, they took the minimum of all N different Hamming distance scores.…”

Section: Comparison To Previous Workmentioning

confidence: 99%

“…We postulated that employing similar techniques for iris recognition could also yield improved performance. There is some prior research in iris recognition that uses multiple still images; for example, [3,4,5,6]. However, no researchers have published on how to use additional information available in iris video.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Image Averaging for Improved Iris Recognition

Hollingsworth

Bowyer

Flynn

2009

Advances in Biometrics

View full text Add to dashboard Cite

Abstract. We take advantage of the temporal continuity in an iris video to improve matching performance using signal-level fusion. From multiple frames of an iris video, we create a single average image. Our signal-level fusion method performs better than methods based on single still images, and better than previously published multi-gallery scorefusion methods. We compare our signal fusion method with another new method: a multi-gallery, multi-probe score fusion method. Between these two new methods, the multi-gallery, multi-probe score fusion has slightly better recognition performance, while the signal fusion has significant advantages in memory and computation requirements.

show abstract