Efficient fingerprint matching using GPU

Ghafoor, Mubeen; Iqbal, Shahzaib; Tariq, Syed Ali; Taj, Imtiaz A.; Jafri, Noman

doi:10.1049/iet-ipr.2016.1021

Cited by 10 publications

(5 citation statements)

References 33 publications

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“…facial recognition deep neural networks [181]), highlighting possibilities of hardware‐acceleration use beyond efficient biometric identification, more specifically in the algorithm training phase, e.g. for iris [182], fingerprint [183], face [184], and sclera vein [185], similarly to FPGAs (see Section 3.6.1), speed‐ups over traditional CPU‐based algorithms of up to two orders of magnitude were reported.…”

Section: Computational Workload Reduction Approachesmentioning

confidence: 99%

Computational workload in biometric identification systems: an overview

2019

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Computational workload is one of the key challenges in biometric identification systems. The naïve retrieval method based on an exhaustive search becomes impractical with the growth of the number of the enrolled data subjects. Consequently, in recent years, many methods with the aim of reducing or optimising the computational workload, and thereby speeding-up the identification transactions, in biometric identification systems have been developed. In this article, a taxonomy for conceptual categorisation of such methods is presented, followed by a comprehensive survey of the relevant academic publications, including computational workload reduction and software/hardware-based acceleration. Lastly, the pertinent technical considerations and trade-offs of the surveyed methods are discussed, along with an industry perspective, and open issues/challenges in the field.

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Section: Computational Workload Reduction Approachesmentioning

confidence: 99%

Computational workload in biometric identification systems: an overview

2019

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“…However, to efficiently use the two applications, people need to require one or more processors that can perform a large number of calculations and operations. Then, it is very critical to decide the hardware devices such as central processing unit (CPU) or graphics processing unit (GPU), which can do parallel calculations [1] and choose proper calculation ways such as distributed computation or cloud computing to decrease the consumption of limited resources. Thus, find the ways such as multi‐parties computation to decrease the consumption of hardware which is also an important issue that we have to be focused on.…”

Section: Introductionmentioning

confidence: 99%

“…1: Define a function, d (x), which is the equation of t − 1 degrees shown in (1) where {c 1 , c 2 , … , c t −1 } are random coefficients.…”

Section: Introductionmentioning

confidence: 99%

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Application of multi‐party computation and error correction with image enhancement and convolution neural networks based on cloud computing

Liao

Peng

Hou

2023

IET Image Processing

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With the development of technology, people work hard on image processing and artificial intelligence which require a lot of computer resources. Instead of simply speeding up calculations with processors, people develop the technology of the Cloud with the Internet which performs high‐quality calculations and has quite a huge amount of storage. Thus, in this paper, the authors use a kind of encryption methods, Secure Multi‐Party Computation (SMPC), to protect the important information which is based on the absence of a trusted party and does not use specific keys or methods to maintain the confidentiality of data. Furthermore, the authors also use the error correction, Berlekamp–Welch (BW) algorithm, to double‐check the correction of the information. To apply the two algorithms to image enhancement and convolution neural networks (CNN), the authors will also discuss the homomorphism in all operations, including addition, multiplication, division etc., and find the best way to encrypt and decrypt the information. Finally, the authors will implement the system with cloud computing to decrease the consumption of computer resources and build a system that can do image enhancement and CNN.

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“…The importance of biometrics is in the fact that the anatomical and behavioral characteristics of an individual cannot be misplaced, shared, or copied. Among all biometric technologies, fingerprint analysis is universally used due to its easy acquisition, high distinctiveness, persistence, and acceptance by the public [2,3]; however, other technologies, such as ECG waveform features, are also found in the literature [4]. Fingerprint recognition has been used in home safety, forensic applications, smartphones, security boxes, smart homes, and even as a work or classroom attendance system [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Portable and Efficient Fingerprint Authentication System Based on a Microcontroller

Vargas¹,

Hoyos²,

Candelo³

2019

IJECE

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This paper presents the design of a fingerprint authentication system based on a simple microcontroller and the fingerprint sensor. The circuit diagram and details regarding the procedure are included. The system was programed in MPLAB and then embedded into the microcontroller. Communication between the PIC and sensor is by RS232 protocol. The results show that the system recognizes the fingerprint in less than 1 second. It is portable and there is no need for image processing. Furthermore, the system shows a high effectiveness when storing and verifying fingerprints.

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Efficient fingerprint matching using GPU

Cited by 10 publications

References 33 publications

Computational workload in biometric identification systems: an overview

Computational workload in biometric identification systems: an overview

Application of multi‐party computation and error correction with image enhancement and convolution neural networks based on cloud computing

Portable and Efficient Fingerprint Authentication System Based on a Microcontroller

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