Texture based features for robust palmprint recognition: a comparative study View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-12

AUTHORS

R. Raghavendra, Christoph Busch

ABSTRACT

Palmprint is a widely used biometric trait deployed in various access-control applications due to its convenience in use, reliability, and low cost. In this paper, we propose a novel scheme for palmprint recognition using a sparse representation of features obtained from Bank of Binarized Statistical Image Features (B-BSIF). The palmprint image is characterized by a rich set of features including principal lines, ridges, and wrinkles. Thus, the use of an appropriate texture descriptor scheme is expected to capture this information accurately. To this extent, we explore the idea of B-BSIF that comprises of 56 different BSIF filters whose responses on the given palmprint image is processed independently and classified using sparse representation classifier (SRC). Extensive experiments are carried out on three different large-scale publicly available palmprint databases. We then present an extensive analysis by comparing the proposed scheme with seven different contemporary state-of-the-art schemes that reveals the efficacy of the proposed scheme for robust palmprint recognition. More... »

PAGES

5

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s13635-015-0022-z

DOI

http://dx.doi.org/10.1186/s13635-015-0022-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1045002624


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