Efficient and Secure Fingerprint Verification for Embedded Devices

Yang, Shenglin; Sakiyama, Kazuo; Verbauwhede, Ingrid

doi:10.1155/asp/2006/58263

Cited by 12 publications

(6 citation statements)

References 18 publications

(18 reference statements)

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“…Based on optimizations at algorithm, hardware and software levels, it is possible to achieve an execution time performance for the complete recognition process, that is, fingerprint acquisition, feature extraction and matching tasks, of less than 4 seconds, which represents a speed-up of ×153.8 with regard to the initial execution time. The execution time optimization ratio achieved in our work when implementing the fingerprint recognition module by means of hardware-software co-design techniques results higher than in [53]. c) In [54] authors implement a fingerprint recognition system able to perform the complete recognition process in less than 200 ms on a Virtex-E FPGA with more than 2M system gates.…”

Section: Performance Evaluation Under Sopc Platformmentioning

confidence: 78%

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FPGA-based Personal Authentication Using Fingerprints

Fons

Cantó

et al. 2011

J Sign Process Syst

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The current technological age demands the deployment of biometric security systems not only in those stringent and highly reliable fields (forensic, government, banking, etc.) but also in a wide range of daily use consumer applications (internet access, border control, health monitoring, mobile phones, laptops, etc.) accessible worldwide to any user. In order to succeed in the exploitation of biometric applications over the world, it is needed to make research on powerefficient and cost-effective computational platforms able to deal with those demanding image and signal operations carried out in the biometric processing. The present work deals with the evaluation of alternative system architectures to those existing PC (personal computers), HPC (high-performance computing) or GPU-based (graphics processing unit) platforms in one specific scenario: the physical implementation of an AFAS (automatic fingerprint-based authentication system) application. The development of automated fingerprint-based personal recognition systems in the way of compute-intensive and real-time embedded systems under SoPC (system-on-programmable-chip) devices featuring one general-purpose MPU (microprocessor unit) and one run-time reconfigurable FPGA (field programmable gate array) proves to be an efficient and cost-effective solution. The provided flexibility, not only in terms of software but also in terms of hardware thanks to the programmability and run-time reconfigurability performance exhibited by the suggested FPGA device, permits to build any application by means of hardware-software co-design techniques. The parallelism and acceleration performances inherent to the hardware design and the ability of reusing hardware resources along the application execution time are key factors to improve the performance of existing systems.

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Section: Performance Evaluation Under Sopc Platformmentioning

confidence: 78%

“…Unlike our work, no real-time performance is reached in [52], but acceleration ratios in the range of ×17.1 are achieved. b) In [53] authors are able to implement a whole AFAS application in a 50 MHz embedded system based on…”

Section: Performance Evaluation Under Sopc Platformmentioning

confidence: 99%

FPGA-based Personal Authentication Using Fingerprints

Fons

Cantó

et al. 2011

J Sign Process Syst

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“…It is noted that for minutia-based solutions the performance is compromised for features with less than 500 bytes, as reported in [ 35 ] where 1000 bytes are employed (by assuming 80 minutiae and 12.5 bytes for each minutia). The minutia-based feature of the embedded fingerprint recognition solution reported in [ 36 ] employs an average size of 512 bytes.…”

Section: Resultsmentioning

confidence: 99%

“…In most of proposals in the literature, the wearables are employed for biometric data acquisition but the recognition algorithm is performed outside the wearable because the algorithms are computationally complex [ 49 , 50 , 51 , 52 ]. The fingerprint-based solutions implemented as software in embedded devices (like [ 36 ]) have a high power consumption or response time that is not suitable for many sensors, as was shown in Table 1 . In order to reduce power consumption and response time, other authors design dedicated hardware for fingerprint recognition [ 53 , 54 , 55 ].…”

Section: Resultsmentioning

confidence: 99%

A PUF- and Biometric-Based Lightweight Hardware Solution to Increase Security at Sensor Nodes

Arjona

Prada-Delgado

Arcenegui

et al. 2018

Sensors

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Security is essential in sensor nodes which acquire and transmit sensitive data. However, the constraints of processing, memory and power consumption are very high in these nodes. Cryptographic algorithms based on symmetric key are very suitable for them. The drawback is that secure storage of secret keys is required. In this work, a low-cost solution is presented to obfuscate secret keys with Physically Unclonable Functions (PUFs), which exploit the hardware identity of the node. In addition, a lightweight fingerprint recognition solution is proposed, which can be implemented in low-cost sensor nodes. Since biometric data of individuals are sensitive, they are also obfuscated with PUFs. Both solutions allow authenticating the origin of the sensed data with a proposed dual-factor authentication protocol. One factor is the unique physical identity of the trusted sensor node that measures them. The other factor is the physical presence of the legitimate individual in charge of authorizing their transmission. Experimental results are included to prove how the proposed PUF-based solution can be implemented with the SRAMs of commercial Bluetooth Low Energy (BLE) chips which belong to the communication module of the sensor node. Implementation results show how the proposed fingerprint recognition based on the novel texture-based feature named QFingerMap16 (QFM) can be implemented fully inside a low-cost sensor node. Robustness, security and privacy issues at the proposed sensor nodes are discussed and analyzed with experimental results from PUFs and fingerprints taken from public and standard databases.

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“…Shenglin Yang et al [29] created another efficient and secure fingerprint verification module for the embedded devices. The authors explored the methodology of hardware and software consign that gained a 65% execution time reduction with less than half the energy consumption compared with the original system.…”

Section: B Related Workmentioning

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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Efficient and Secure Fingerprint Verification for Embedded Devices

Cited by 12 publications

References 18 publications

FPGA-based Personal Authentication Using Fingerprints

FPGA-based Personal Authentication Using Fingerprints

A PUF- and Biometric-Based Lightweight Hardware Solution to Increase Security at Sensor Nodes

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

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