A fast and accurate method for detecting fingerprint reference point

Guo, Xifeng; Zhu, En; Yin, Jianping

doi:10.1007/s00521-016-2285-9

Cited by 14 publications

(6 citation statements)

References 33 publications

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“…As power consumption(inversely proportional to the energy efficiency) of the hardware accelerator is directly determined by the fabrication technology processes, technology scaling is considered to ensure the fair comparisons of the energy efficiency. For technology scaling, the definition of S and the normalization of power consumption are (7,8) from [39]. As a result, energy efficiency of the vector-SIMD DSP Matrix2000 is normalized to 16nm to be compared with the GPUs on the same fabrication technology in Figure 13.…”

Section: Resultsmentioning

confidence: 99%

“…In recent years, deep convolutional neural network (DCNN) algorithms have been widely adopted for computer vision tasks including image classification, object detection, semantic segmentation and so on [1]- [8]. In large scale visual recognition challenge (ILSVRC) 2012, AlexNet [9] achieved the best results, making DCNN the core algorithm model for image classification.…”

Section: Introductionmentioning

confidence: 99%

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The Evaluation of DCNN on Vector-SIMD DSP

et al. 2019

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In current computer vision tasks, deep convolutional neural networks (DCNN) achieve stateof-the-art results. At present, the DCNN has been widely used in many usage scenarios. It is also important to develop DCNN on supercomputers in the age of artificial intelligence. As a general-purpose accelerator deployed in a supercomputer which ranks the fourth in the latest Top500 list, the Matrix2000 is a vector single instruction multiple data (vector-SIMD) digital signal processor (DSP). The research of DCNN implementation based on DSP is important and meaningful. Till now, there are few systematic studies published on DCNN implementation based on DSP. Qualcomm, CEVA, and Cadence have said that they have implemented DCNN on their own DSP. However, Qualcomm and CEVA have not published how they implemented DCNN based on their own DSP and Cadence has only implemented a convolution layer based on Vision P6. In this paper, we proposed a vectorization mapping method and a high efficient partition analysis model for implementing DCNN on vector-SIMD DSP. Based on the vectorization mapping method and analysis model, we implemented all layers of typical DCNN models on Matrix2000 and tested the computation and energy efficiency. The experiments demonstrate that the average computation efficiency of this paper based on Matrix2000 is 20∼35% higher than GPU, 35∼45% higher than Xeon Phi, about 8% higher than Vision P6 DSP, about 62∼75% higher than an existing evaluation of DCNN based on Matrix2000, and the average energy efficiency is about 9∼30% higher than GPU, and about 56% higher than the existing evaluation of DCNN based on Matrix2000. The results show that the vector-SIMD DSP with a suitable programming mapping method is also a suitable platform in the age of artificial intelligence.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Evaluation of DCNN on Vector-SIMD DSP

et al. 2019

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“…We referred to the methods used in fingerprint pattern classification field which is mainly based on smoothing processing. The refining method achieved a good result in 2D case [24], [30], [31]. We generalize the method to 3D manifold.…”

Section: Noise Deductionmentioning

confidence: 89%

Triangular Model Registration Algorithm Through Differential Topological Singularity Points by Helmholtz-Hodge Decomposition

et al. 2019

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Iterative closest point algorithms suffer from non-convergence and local minima when dealing with cloud points with a different sampling density. Alternative global or semi-global registration algorithms may suffer from efficiency problem. This paper proposes a new registration algorithm through the differential topological singularity points (DTSP) based on the Helmholtz-Hodge decomposition (HHD), which is called DTSP-ICP method. The DTSP-ICP method contains two algorithms. First, the curvature gradient fields on surfaces are decomposed by the HHD into three orthogonal parts: divergence-free vector field, curl-free vector field, and a harmonic vector field, and then the DTSP algorithm is used to extract the differential topological singularity points in the curl-free vector field. Second, the ICP algorithm is utilized to register the singularity points into one aligned model. The singularity points represent the feature of the whole model, and the DTSP algorithm is designed to capture the nature of the differential topological structure of a mesh model. Through the singularity alignment, the DTSP-ICP method, therefore, possesses better performance in triangular model registration. The experimental results show that independent of sampling schemes, the proposed DTSP-ICP method can maintain convergence and robustness in cases where other alignment algorithms including the ICP alone are unstable. Moreover, this DTSP-ICP method can avoid the local errors of model registration based on Euclidean distance and overcome the computation insufficiencies observed in other global or semi-global registration publications. Finally, we demonstrate the significance of the DTSP-ICP algorithm's advantages on a variety of challenging models through result comparison with that of two other typical methods. INDEX TERMSIterative closest point algorithm, computer aided manufacturing, differential equations, HHD.

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“…The proposed approach achieved 59% accuracy on the subset of the mentioned databases. An extension of the previous approach has been proposed in [3]. While walking on the singularity, the location of the detected singular point is further refined using an enhancement method based on the mean-shift concept.…”

Section: Related Workmentioning

confidence: 99%

SP-NET: One Shot Fingerprint Singular-Point Detector

Arora,

Jha,

Agrawal

et al. 2019

Preprint

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Singular points of a fingerprint image are special locations having high curvature properties. They can play a pivotal role in fingerprint normalization and reliable feature extraction. Accurate and efficient extraction of a singular point plays a major role in successful fingerprint recognition and indexing. In this paper, a novel deep learning based architecture is proposed for one shot (end-to-end) singular point detection from an input fingerprint image. The model consists of a Macro-Localization Network and a Micro-Regression Network along with three stacked hourglass as a bottleneck. The proposed model has been tested on three databases viz. FVC2002 DB1_A, FVC2002 DB2_A and FPL30K and has been found to achieve true detection rate of 98.75%, 97.5% and 92.72% respectively, which is better than any other state-of-the-art technique.

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A fast and accurate method for detecting fingerprint reference point

Cited by 14 publications

References 33 publications

The Evaluation of DCNN on Vector-SIMD DSP

The Evaluation of DCNN on Vector-SIMD DSP

Triangular Model Registration Algorithm Through Differential Topological Singularity Points by Helmholtz-Hodge Decomposition

SP-NET: One Shot Fingerprint Singular-Point Detector

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