An Efficient Finger-Knuckle-Print Based Recognition System Fusing SIFT and SURF Matching Scores

  • G. S. Badrinath
  • Aditya Nigam
  • Phalguni Gupta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7043)

Abstract

This paper presents a novel combination of local-local information for an efficient finger-knuckle-print (FKP) based recognition system which is robust to scale and rotation. The non-uniform brightness of the FKP due to relatively curvature surface is corrected and texture is enhanced. The local features of the enhanced FKP are extracted using the scale invariant feature transform (SIFT) and the speeded up robust features (SURF). Corresponding features of the enrolled and the query FKPs are matched using nearest-neighbour-ratio method and then the derived SIFT and SURF matching scores are fused using weighted sum rule. The proposed system is evaluated using PolyU FKP database of 7920 images for both identification mode and verification mode. It is observed that the system performs with CRR of 100% and EER of 0.215%. Further, it is evaluated against various scales and rotations of the query image and is found to be robust for query images downscaled upto 60% and for any orientation of query image.

Keywords

Query Image Scale Invariant Feature Transform Equal Error Rate Scale Invariant Feature Transform Feature Correct Recognition Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • G. S. Badrinath
    • 1
  • Aditya Nigam
    • 1
  • Phalguni Gupta
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of TechnologyKanpurIndia

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