Abstract
The fingerprint identification is an efficient biometric technique to authenticate human beings in real-time Big Data Analytics. In this paper, we propose an efficient Finite State Machine (FSM) based reconfigurable architecture for fingerprint recognition. The fingerprint image is resized, and Compound Linear Binary Pattern (CLBP) is applied on fingerprint, followed by histogram to obtain histogram CLBP features. Discrete Wavelet Transform (DWT) Level 2 features are obtained by the same methodology. The novel matching score of CLBP is computed using histogram CLBP features of test image and fingerprint images in the database. Similarly, the DWT matching score is computed using DWT features of test image and fingerprint images in the database. Further, the matching scores of CLBP and DWT are fused with arithmetic equation using improvement factor. The performance parameters such as TSR (Total Success Rate), FAR (False Acceptance Rate), and FRR (False Rejection Rate) are computed using fusion scores with correlation matching technique for FVC2004 DB3 Database. The proposed fusion based VLSI architecture is synthesized on Virtex xc5vlx30T-3 FPGA board using Finite State Machine resulting in optimized parameters.
Highlights
The reliable personnel authentication [1, 2] based on biometrics has significant importance in the present digital world and can be achieved by human and computer interface activities
The biometric physiological or behavioural samples are captured using sensors or devices, which are further processed in the level of vetting through Office for Personal Management (OPM) which can be either verification or identification
The contribution and novel aspects of the proposed techniques are listed as follows: (i) the computation of the novel matching score for Compound Linear Binary Pattern (CLBP) and Discrete Wavelet Transform (DWT) features; (ii) the matching score values which are varied based on characteristics of images, that is, the values which are computed adaptively based on characteristics of the images; (iii) the fusion of matching scores with improvement factor; (iv) the implementation of Finite State Machine (FSM) based VLSI architecture to improve the hardware performance
Summary
The reliable personnel authentication [1, 2] based on biometrics has significant importance in the present digital world and can be achieved by human and computer interface activities. The efficient FPGA architectures [17,18,19,20] for fingerprint processing and existing algorithms to identify a fingerprint based on minutiae [21], ridge, multiresolution features, and Hough transform were discussed. Vatsa et al [22] proposed Redundant Discrete Wavelet Transform based on local image quality assessment algorithm followed by extraction algorithm using Level 3 features. These features are combined with Level 1 and Level 2 in the fingerprint identification scheme. Paulino et al [34] proposed an alignment algorithm (descriptor-based Hough transform) for latent fingerprint matching This technique measures similarity between fingerprints by considering both minutiae and orientation field information. The contribution and novel aspects of the proposed techniques are listed as follows: (i) the computation of the novel matching score for CLBP and DWT features; (ii) the matching score values which are varied based on characteristics of images, that is, the values which are computed adaptively based on characteristics of the images; (iii) the fusion of matching scores with improvement factor; (iv) the implementation of FSM based VLSI architecture to improve the hardware performance
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