A Robust and Efficient Minutia-Based Fingerprint Matching Algorithm

Wen, Wen; Qi, Zhi; Li, Zhi; Zhang, Junhao; Gong, Yu; Cao, Peng

doi:10.1109/acpr.2013.43

Cited by 3 publications

(7 citation statements)

References 14 publications

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“…In order to demonstrate the effectiveness of the new algorithm, some other matching algorithms are used, including the efficient minutia-based fingerprint matching algorithm (simplified as EMF ) [8], the local structural similarity algorithm (simplified as LSS ) [11], the local topologic structure algorithm (simplified as LTS ) [12], the grow and fuse algorithm (simplified as GF ) [14], the minutia cylinder-code algorithm (simplified as MCC ) [15], the minutiae triplets algorithm (simplified as MT ) [16] and the local structure compatibility algorithm (simplified as LSC ) [18]. Here we use only minutia features (coordinates and orientations) for every algorithm.…”

Section: Resultsmentioning

confidence: 99%

“…Take global MTM into consideration, we design two combined algorithms: local MCC + global MTM , and local MTM + global MTM . We compare these two algorithms with some other algorithms, including EMF [8], LSS [11], LTS [12], GF [14], MCC [15], MT [16] and LSC [18]. EMF applied a set of global level minutia dependent features including qualities and the area of overlapping regions.…”

Section: Resultsmentioning

confidence: 99%

“…Izadi et al introduced cylinder quality measure into minutia cylinder-code based fingerprint matching [7]. Wen et al applied a set of global level minutia dependent features, including minutia qualities and the area of overlapping regions [8]. Thuy et al increased the ridge-valley structure features and improved the classical Thin-Plate-Spline deformation model [9], and they proposed the local Thin-Plate-Spline to deal with non-linear distorted fingerprints [10].…”

Section: Introductionmentioning

confidence: 99%

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Minutia Tensor Matrix: A New Strategy for Fingerprint Matching

Feng

2015

PLoS ONE

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Establishing correspondences between two minutia sets is a fundamental issue in fingerprint recognition. This paper proposes a new tensor matching strategy. First, the concept of minutia tensor matrix (simplified as MTM) is proposed. It describes the first-order features and second-order features of a matching pair. In the MTM, the diagonal elements indicate similarities of minutia pairs and non-diagonal elements indicate pairwise compatibilities between minutia pairs. Correct minutia pairs are likely to establish both large similarities and large compatibilities, so they form a dense sub-block. Minutia matching is then formulated as recovering the dense sub-block in the MTM. This is a new tensor matching strategy for fingerprint recognition. Second, as fingerprint images show both local rigidity and global nonlinearity, we design two different kinds of MTMs: local MTM and global MTM. Meanwhile, a two-level matching algorithm is proposed. For local matching level, the local MTM is constructed and a novel local similarity calculation strategy is proposed. It makes full use of local rigidity in fingerprints. For global matching level, the global MTM is constructed to calculate similarities of entire minutia sets. It makes full use of global compatibility in fingerprints. Proposed method has stronger description ability and better robustness to noise and nonlinearity. Experiments conducted on Fingerprint Verification Competition databases (FVC2002 and FVC2004) demonstrate the effectiveness and the efficiency.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Minutia Tensor Matrix: A New Strategy for Fingerprint Matching

Feng

2015

PLoS ONE

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“…According to the survey [19], minutia is the most commonly used features in fingerprint recognition for its locality, compactness, and computational efficiency. Thus, we developed a robust and light minutiae based hierarchical recognition algorithm that exhibits an excellent accuracy and presents as well potentials to serve a real time fingerprint recognition system [27]. The basic work flow of a fingerprint verification system is illustrated in Figure 3, which shows the feature extraction in the left column and the feature matching on the right column.…”

Section: A Algorithm Introductionmentioning

confidence: 99%

“…The global matching procedure applies a hierarchical recognition strategy to refine the local matching decision towards a genuine result over the entire images. Since none of the time consuming operations are involved in our fingerprint recognition procedure, the proposed algorithm [27] is suitable for a real time fingerprint recognition system.…”

Section: A Algorithm Introductionmentioning

confidence: 99%

An energy efficient OpenCL implementation of a fingerprint verification system on heterogeneous mobile device

Wen

Meng

et al. 2014

2014 IEEE 20th International Conference on Embedded and Real-Time Computing Systems and Applications

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With the increasing concerns over the personal privacy of mobile devices, biometrics algorithms plays an important role to enhance the security. As one of the most popular approaches, fingerprint verification as a personal identification interface is widely recognized and adopted by many commercial devices. However, its inherent computational complexity make the algorithm of fingerprint verification difficult to achieve high performance on mobile platforms, such a battery powered, size limited, and producing cost controlled device. In addition to the performance, energy efficiency is also of significant consideration of such a fingerprint verification system. In this paper, we present an energy efficient OpenCL based heterogeneous implementation of the fingerprint verification system on a commercial mobile platform, taking advantage of mobile CPUs and GPUs. We carefully analyze the workloads through system profiling to identify the parallelism then to partition the algorithm between the CPU and GPU . The experimental results show that our GPU implementation of DFT analysis achieves a 1.4X speedup and 36.87% energy reduction compared to the CPU only implementation in the mobile platform. This heterogenous implementation of the entire fingerprint verification system accomplishes 1.32X speedup and 16.70% energy superiority above the CPU only solution. To the best of the authors' knowledge, this work is the first published implementation of OpenCL based fingerprint verification system accelerated by mobile GPUs on a heterogeneous mobile device. We believe our mapping methodology of this fingerprint verification system can be generalized to map more similar applications onto heterogeneous mobile devices.

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