SIFT-based iris recognition revisited: prerequisites, advantages and improvements View Full Text


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Article Info

DATE

2018-05-16

AUTHORS

C. Rathgeb, J. Wagner, C. Busch

ABSTRACT

Scale-invariant feature transform (SIFT), which represents a general purpose image descriptor, has been extensively used in the field of biometric recognition. Focusing on iris biometrics, numerous SIFT-based schemes have been presented in past years, offering an alternative approach to traditional iris recognition, which are designed to extract discriminative binary feature vectors based on an analysis of pre-processed iris textures. However, the majority of proposed SIFT-based systems fails to maintain the recognition accuracy provided by generic schemes. Moreover, traditional systems outperform SIFT-based approaches with respect to other key system factors, i.e. authentication speed and storage requirement. In this work, we propose a SIFT-based iris recognition system, which circumvents the drawbacks of previous proposals. Prerequisites, derived from an analysis of the nature of iris biometric data, are utilized to construct an improved SIFT-based baseline iris recognition scheme, which operates on normalized enhanced iris textures obtained from near-infrared iris images. Subsequently, different binarization techniques are introduced and combined to obtain binary SIFT-based feature vectors from detected keypoints and their descriptors. On the CASIAv1, CASIAv4-Interval and BioSecure iris database, the proposed scheme maintains the performance of different traditional systems in terms of recognition accuracy as well as authentication speed. In addition, we show that SIFT-based features complement those extracted by traditional schemes, such that a multi-algorithm fusion at score level yields a significant gain in recognition accuracy. More... »

PAGES

1-18

References to SciGraph publications

  • 2004-11. Distinctive Image Features from Scale-Invariant Keypoints in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2008. Handbook of Biometrics in NONE
  • 2012. B-SIFT: A Highly Efficient Binary SIFT Descriptor for Invariant Feature Correspondence in INTELLIGENT SCIENCE AND INTELLIGENT DATA ENGINEERING
  • 2005. UBIRIS: A Noisy Iris Image Database in IMAGE ANALYSIS AND PROCESSING – ICIAP 2005
  • 2012. An Iris Recognition Approach with SIFT Descriptors in ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS. WITH ASPECTS OF ARTIFICIAL INTELLIGENCE
  • 2013-08. SIFT based iris recognition with normalization and enhancement in INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
  • 2010. BRIEF: Binary Robust Independent Elementary Features in COMPUTER VISION – ECCV 2010
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    http://scigraph.springernature.com/pub.10.1007/s10044-018-0719-y

    DOI

    http://dx.doi.org/10.1007/s10044-018-0719-y

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    47 schema:description Scale-invariant feature transform (SIFT), which represents a general purpose image descriptor, has been extensively used in the field of biometric recognition. Focusing on iris biometrics, numerous SIFT-based schemes have been presented in past years, offering an alternative approach to traditional iris recognition, which are designed to extract discriminative binary feature vectors based on an analysis of pre-processed iris textures. However, the majority of proposed SIFT-based systems fails to maintain the recognition accuracy provided by generic schemes. Moreover, traditional systems outperform SIFT-based approaches with respect to other key system factors, i.e. authentication speed and storage requirement. In this work, we propose a SIFT-based iris recognition system, which circumvents the drawbacks of previous proposals. Prerequisites, derived from an analysis of the nature of iris biometric data, are utilized to construct an improved SIFT-based baseline iris recognition scheme, which operates on normalized enhanced iris textures obtained from near-infrared iris images. Subsequently, different binarization techniques are introduced and combined to obtain binary SIFT-based feature vectors from detected keypoints and their descriptors. On the CASIAv1, CASIAv4-Interval and BioSecure iris database, the proposed scheme maintains the performance of different traditional systems in terms of recognition accuracy as well as authentication speed. In addition, we show that SIFT-based features complement those extracted by traditional schemes, such that a multi-algorithm fusion at score level yields a significant gain in recognition accuracy.
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