Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/46952
Title: Sclera Recognition - A Survey
Authors: Das, Abhijit
Pal, Umapada
Blumenstein, Michael
Ballester, Miguel Angel Ferrer 
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Iris Recognition
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Tasom
Sclera Biometric
Sclera Recognition, et al
Issue Date: 2013
Journal: Proceedings - 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013
Conference: 2013 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013 
Abstract: This paper presents a survey on sclera-based biometric recognition. Among the various biometric methods, sclera is one of the novel and promising biometric techniques. The sclera, a white region of connective tissue and blood vessels, surrounds the iris. A survey of the techniques available in the area of sclera biometrics will be of great assistance to researchers, and hence a comprehensive effort is made in this article to discuss the advancements reported in this regard during the past few decades. As a limited number of publications are found in the literature, an attempt is made in this paper to increase awareness of this area so that the topic gains popularity and interest among researchers. In this survey, a brief introduction is given initially about the sclera biometric, which is subsequently followed by background concepts, various pre-processing techniques, feature extraction and finally classification techniques associated with the sclera biometric. Benchmarking databases are very important for any pattern recognition related research, so the databases related with this work is also discussed. Finally, our observations, future scope and existing difficulties, which are unsolved in sclera biometrics, are discussed. We hope that this survey will serve to focus more researcher attention towards the emerging sclera biometric.
URI: http://hdl.handle.net/10553/46952
DOI: 10.1109/ACPR.2013.168
Source: Proceedings - 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013 (6778464), p. 917-921
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