Elsevier

Neurocomputing

Volume 151, Part 3, 3 March 2015, Pages 1120-1132
Neurocomputing

Designing an accurate hand biometric based authentication system fusing finger knuckleprint and palmprint

https://doi.org/10.1016/j.neucom.2014.03.083Get rights and content

Abstract

This paper proposes an accurate and efficient multi-modal authentication system that makes use of palm and knuckleprint samples. Biometric images are transformed using the proposed sign of local gradient (SLG). Corner features are extracted from vcode and hcode and are tracked using geometrically and statistically constrained Lucas and Kanade tracking algorithm. The proposed highly uncorrelated features (HUF) measure is used to match two query images. The proposed system is tested on publicly available PolyU and CASIA palmprint databases along with PolyU Knuckleprint database. Several sets of chimeric bi-modal as well as multimodal databases are created in order to test the proposed system. Experimental results reveal that the proposed multi-modal system achieves CRR of 100% with an EER as low as 0.01% over all created chimeric multimodal datasets.

Introduction

There is a need to have automated, secure and accurate human access control mechanisms for reliable identification of people in several social applications such as law enforcement, secure banking, immigration control, etc. The best mode in which the identity management can be realized is the biometric based authentication system which uses physiological (fingerprint [9], [37], face [41], [40], [26], [27], iris [11], [29], etc.) or behavioral (signature, gait, etc.) characteristics. Biometrics based solutions are better than the traditional token or knowledge based identification systems as they are harder to spoof, easier to use and never be lost. In past few years, society have noticed great attention in hand based biometric recognition systems (e.g. palm print [5], fingerprint [9] and finger knuckleprint [47], [28], [30]) because of their low cost acquisition sensors, high performance, higher user acceptance and lesser need of user cooperation. The pattern formations at finger knuckle bending [47] as well as palmprint region [5] are supposed to be stable (as shown in Fig. 1) and hence can be considered as discriminative biometric traits.

Palmprint: The inner part of the hand is called palm and the extracted region of interest in between fingers and wrist is termed as palmprint as shown in Fig. 1(a). Pattern formation within this region are suppose to be stable as well as unique. Even monozygotic twins are found to have different palmprint patterns [16]. Hence one can consider it as a well-defined and discriminative biometrics trait. Palmprint׳s prime advantages over fingerprint includes its higher social acceptance because it is never being associated with criminals and larger ROI area as compared to fingerprint. Larger ROI ensures abundance of structural features including principle lines, wrinkles, creases and texture pattern (as it is evident in Fig. 1(a)) even in low resolution palmprint images. This enhances system׳s speed, accuracy and reduces the cost. Some other factors favoring palmprint are lesser user cooperation, non-intrusive and cheaper acquisition sensors.

Knuckleprint: The horizontal and the vertical pattern formation in finger knuckleprint images (as shown in Fig. 1(b)) are believed to be very discriminative [47]. The knuckleprint texture is developed very early and lasts very long primarily because they are on the outer side of the hand, hence safely preserved. Its failure to enroll rate (FTE) is observed to be lower as compared to fingerprint and can be acquired easily using an inexpensive setup with lesser user cooperation. The user acceptance favors knuckleprint as unlike fingerprint they are never being associated to any criminal investigations. A comparative study between palmprint and knuckleprint based over the biometric properties is presented in Table 1.

Multimodal: The performance of any unimodal biometric system is often got restricted by variable and uncontrolled environmental conditions, sensor precision and reliability. Several trait specific challenges such as pose, expression, aging, etc. for face recognition degrades the system performance. Hence they can only provide low or middle level security. Fusing more than one biometric traits in pursuit of superior performance can be a very useful idea, termed as multi-modal [10] systems. Any such system makes use of multiple biometric traits to enhance system׳s performance especially when a huge number of subjects are enrolled. The false acceptance rate grows rapidly with the database size [4]; hence multiple trait data can be utilized to achieve better performance.

In this paper palmprint and knuckleprint ROI׳s are extracted and transformed using the proposed sign of local gradient (SLG) method to obtain robust vcode and hcode image representations. Corner features are extracted from vcode and hcode by performing eigen analysis of the Hessian matrix at every pixel. The matching is performed using the proposed HUF dissimilarity measure. Finally scores obtained for both traits (i.e. palm and knuckleprint) are fused to get multi-modal fusion score using the SUM rule. The overall architecture of the proposed multi-modal biometric system is shown in Fig. 2.

This paper is organized as follows: the comprehensive literature survey is presented in Section 2. In Section 3 extraction of region of interest (ROI) from biometric sample is explained. Section 4 describes the proposed algorithm. Section 5 presents the detailed experimental results of the proposed system on publicly available palmprint and knuckleprint along with their fused self-created chimeric multimodal databases. Last section presents the concluding remarks.

Section snippets

Palmprint

Palmprint recognition systems are broadly based on structural or statistical features. In [13], line-like structural features are extracted by applying morphological operations over edge-maps. In [12], structural features such as points on principle line and some isolated points are utilized for palmprint authentication. In [44], single fixed orientation Gabor filter is applied over the palmprint and the resulting Gabor phase is binarized using zero crossing. In [15], bank of elliptical Gabor

ROI extraction from biometric sample

The first step in any biometric based authentication system is region of interest (ROI) extraction. In this work palm and knuckleprint ROI׳s are extracted using the algorithms proposed in [5], [46].

Proposed system

The details of the proposed authentication system are discussed in this section.

Databases

The proposed system is tested on two publicly available benchmark palmprint databases CASIA [2] and PolyU [3] and the largest publicly available PolyU knuckleprint database [1].

Conclusion

This paper presents a multi-modal fusion based biometric system using highly uncorrelated features (HUF) dissimilarity measure to compare structural features for palm and knuckleprints images. Robustness against varying illumination is achieved by working over edge-maps. The ROI images are transformed using the sign of local gradient SLG method that works over edge-map to obtain more discriminative vcode and hcode representations. The corner features are extracted from vcode and hcode and are

Aditya Nigam has received MTech degree in Computer Science and Engineering from Indian Institute of Technology, Kanpur, India, in 2009. He is currently a Ph.D. student in the Department of Computer Science and Engineering at Indian Institute of Technology, Kanpur, India. His research interest includes biometrics, pattern recognition, computer vision and image processing.

References (48)

  • Knuckleprint polyu....
  • Palmprint casia....
  • Palmprint polyu....
  • S.C. Amit J. Mhatre, Srinivas Palla, V. Govindaraju, Efficient search and retrieval in biometric databases, in: Proc....
  • G. Badrinath, P. Gupta, Palmprint based recognition system using phase-difference information, Future Gener. Comput....
  • G.S. Badrinath et al.

    Verification system robust to occlusion using low-order zernike moments of palmprint sub-images

    Telecommun. Syst.

    (2011)
  • G.S. Badrinath, K. Tiwari, P. Gupta, An efficient palmprint based recognition system using 1d-dct features, in:...
  • R. Cappelli et al.

    Minutia cylinder-codea new representation and matching technique for fingerprint recognition

    IEEE Trans. Pattern Anal. Mach. Intell.

    (2010)
  • R. Connaughton et al.

    Fusion of face and iris biometrics

  • J. Daugman

    High confidence visual recognition of persons by a test of statistical independence

    IEEE Trans. Pattern Anal. Mach. Intell.

    (1993)
  • A.W.K. Kong, D. Zhang, Competitive coding scheme for palmprint verification, in: International Conference on Pattern...
  • A.W.K. Kong, D. Zhang, Feature-level fusion for effective palmprint authentication, in: International Conference of...
  • A. Kumar et al.

    Personal authentication using finger knuckle surface

    IEEE Trans. Inf. Forensics Secur.

    (2009)
  • A. Kumar et al.

    Personal recognition using hand shape and texture

    IEEE Trans. Image Process.

    (2006)
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    Aditya Nigam has received MTech degree in Computer Science and Engineering from Indian Institute of Technology, Kanpur, India, in 2009. He is currently a Ph.D. student in the Department of Computer Science and Engineering at Indian Institute of Technology, Kanpur, India. His research interest includes biometrics, pattern recognition, computer vision and image processing.

    Phalguni Gupta did his Ph.D. from IIT Kharagpur and started his career in 1983 by joining in Space Applications Centre (ISRO) Ahmedabad, India as a Scientist. In 1987, he joined the Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, India. Currently he is a Professor in the CSE department. He works in the field of data structures, sequential algorithms, parallel algorithms, on-line algorithms, image analysis, and biometrics. He has published more than 350 papers in International Journals and Conferences. He has completed several sponsored and consultancy projects which are funded by the Government of India. Some of these projects are in the area of Biometrics, System Solver, Grid Computing, Image Processing, Mobile Computing, and Network Flow. During this period he has proved himself a well-known researchers in theoretical computer science, especially in the field of biometrics.

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