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Biometric Quality: Analysis of Iris Recognition Techniques with other Biometric Authentication Systems

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Prashant Kapoor, Paresh Rawat

Prashant Kapoor and Paresh Rawat. Biometric Quality: Analysis of Iris Recognition Techniques with other Biometric Authentication Systems. International Journal of Computer Applications 162(4):31-36, March 2017. BibTeX

	author = {Prashant Kapoor and Paresh Rawat},
	title = {Biometric Quality: Analysis of Iris Recognition Techniques with other Biometric Authentication Systems},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2017},
	volume = {162},
	number = {4},
	month = {Mar},
	year = {2017},
	issn = {0975-8887},
	pages = {31-36},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2017913280},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


A wide variety of systems require reliable personal recognition schemes to either conform or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that only a legitimate user, access the rendered service. In this paper, many Biometric authentications will be studied and conclude that Iris biometric is effective, fast and reliable for the person recognition compare to any other well-known biometrics. This paper presents a survey of different concepts and interpretations of biometric quality. Several factors that cause different types of degradations of biometric samples, including image features that attribute to the effects of these degradations, are discussed. Evaluation schemes [1] are presented to test the performance of quality metrics for various applications. A survey of the features, strengths, and limitations of existing quality assessment techniques in iris, iris, and face biometric are also presented. Finally, a representative set of quality metrics from these three modalities are evaluated on a multimodal database consisting of 2D images [2], to understand their behavior with respect to match scores obtained from the state-of-the-art recognition systems.


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Biometric, Segmentation, Normalization, Iris Tissue Encoding and Matching