An Optimized Face Recognition for Edge Computing

Xie, Yuan; Ding, Luchang; Zhou, Aaron; Chen, Gengsheng

doi:10.1109/asicon47005.2019.8983596

Cited by 16 publications

(10 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Raspberry pi in the system didn't exceed 1 FPS and the other two processors have outperformed both algorithms. However, in [92] the use of hybrid accelerators has decreased the detection time and improved the raspberry pi processing time performance. In the recognition process, the raspberry pi has an average of 1.5 FPS processing rate when neural network algorithms are used [76] [85].…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

“…In the recognition process, the raspberry pi has an average of 1.5 FPS processing rate when neural network algorithms are used [76] [85]. However, the average value is exceeded in [92] and [37]. In [92] the raspberry pi is used along with Intel Movidius which improved the processing time performance.…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

“…However, the average value is exceeded in [92] and [37]. In [92] the raspberry pi is used along with Intel Movidius which improved the processing time performance. Whereas, in [37] a 5 FPS processing rate was achieved using the non-neural network PCA algorithm.…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

See 2 more Smart Citations

Hardware Accelerators for Real-Time Face Recognition: A Survey

2022

View full text Add to dashboard Cite

Real-time face recognition has been of great interest in the last decade due to its wide and variant critical applications which include biometrics, security in public places, and identification in login systems. This has encouraged researchers to design fast and accurate embedded and portable systems that are capable to detect and recognize a large number of faces at almost video frame rate. Due to the increasing volume of reference faces, traditional general purpose computing engines such as the ones based on Intel's Pentium processors have shown not to be adequate and various dedicated hardware accelerators based on either Graphical Processing Unit (GPU), Field Programmable Gate Arrays (FPGA), Application Specific Integrated Circuit (ASIC), or even multi-core Central Processing Units (CPU) have emerged. Earlier published review papers on face detection/recognition have discussed face detection and face recognition algorithms enhancement that improve the detection accuracy. Nevertheless, none of them has reviewed the hardware accelerators used for this application. Accordingly, this paper aims to provide a comprehensive review of the most recent face recognition algorithms and associated embedded hardware systems targeting real-time performance. A detailed comparison between neural network and non-neural network-based algorithms in terms of accuracy and processing time is provided. Discussions on their suitability to be implemented into parallel hardware architectures such as Single Instruction Multiple Thread (SIMT) or Single Instruction Multiple Data (SIMD) is also discussed.

show abstract

Section: Discussion and Recommendationsmentioning

confidence: 99%

Section: Discussion and Recommendationsmentioning

confidence: 99%

See 1 more Smart Citation

Hardware Accelerators for Real-Time Face Recognition: A Survey

2022

View full text Add to dashboard Cite

show abstract

“…That is the case of the work by Yuan Xie et al. [40] , where they analyze the overall speedup of Sphereface using Mobilenet on a hardware configuration consisting of a Raspberry Pi 3B+ with an NCS2, obtaining performance of 7.031 FPS. In this paper, the focus is not set in analysing the performance of the NCS2 with face recognition algorithms but in mitigating the loss of performance in multiface scenarios with a cluster hardware configuration of NCS2.…”

Section: Related Workmentioning

confidence: 99%

CNN Inference acceleration using low-power devices for human monitoring and security scenarios

Mas

Panadero²,

Botella

et al. 2020

Computers & Electrical Engineering

View full text Add to dashboard Cite

Security is currently one of the top concerns in our society. From governmental installations to private companies and medical institutions, they all have to address directly with security issues as: access to restricted information quarantine control, or criminal tracking. As an example, identifying patients is critical in hospitals or geriatrics in order to isolate infected people, which has proven to be a non- trivial issue with the COVID-19 pandemic that is currently affecting all countries, or to locate fled patients. Face recognition is then a non-intrusive alternative for performing these tasks. Although FaceNet from Google has proved to be almost perfect, in a multi-face scenario its performance decays rapidly. In order to mitigate this loss of performance, in this paper a cluster based on the Neural Computer Stick version 2 and OpenVINO by Intel is proposed. A detailed power and runtime study is shown for two programming models, namely: multithreading and multiprocessing. Furthermore, 3 different hosts have been considered. In the most efficient configuration, an average of 6 frames per second has been achieved using the Raspberry Pi 4 as host and with a power consumption of just 11.2W, increasing by a factor of 3.3X the energy efficiency with respect to a PC-based solution in a multi-face scenario.

show abstract

“…Nowadays, user equipment terminals, such as smart mobile phones, smart sensors, smart bands, virtual reality (VR) glass, wearable devices, smart watches, and smart cameras are growing in popularity [1][2][3][4]. The high-demanding applications and services, such as mobile augmented reality, gesture and face recognition, intelligent transportation, smart healthcare, interactive gaming, human heart-rate monitoring, voice recognition, natural language processing, and wearable virtual reality streaming are undergoing tremendous developments [5][6][7][8][9]. With the enormous enhancement of smart user devices and the emergence of recent innovative applications and high-demanding services, data traffic is rising exponentially [10,11].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Based Collaborative Task Offloading Scheme in the Densely Deployed Small-Cell Networks with Multi-Access Edge Computing

et al. 2020

View full text Add to dashboard Cite

Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-access edge computing (MEC) in a small-cell network (SCN) is designed to provide computation-intensive and latency-sensitive applications through task offloading. However, without collaboration, the resources of a single MEC server are wasted or sometimes overloaded for different service requests and applications; therefore, it increases the user’s task failure rate and task duration. Meanwhile, the distinct MEC server has faced some challenges to determine where the offloaded task will be processed because the system can hardly predict the demand of end-users in advance. As a result, the quality-of-service (QoS) will be deteriorated because of service interruptions, long execution, and waiting time. To improve the QoS, we propose a novel Fuzzy logic-based collaborative task offloading (FCTO) scheme in MEC-enabled densely deployed small-cell networks. In FCTO, the delay sensitivity of the QoS is considered as the Fuzzy input parameter to make a decision where to offload the task is beneficial. The key is to share computation resources with each other and among MEC servers by using fuzzy-logic approach to select a target MEC server for task offloading. As a result, it can accommodate more computation workload in the MEC system and reduce reliance on the remote cloud. The simulation result of the proposed scheme show that our proposed system provides the best performances in all scenarios with different criteria compared with other baseline algorithms in terms of the average task failure rate, task completion time, and server utilization.

show abstract

An Optimized Face Recognition for Edge Computing

Cited by 16 publications

References 3 publications

Hardware Accelerators for Real-Time Face Recognition: A Survey

Hardware Accelerators for Real-Time Face Recognition: A Survey

CNN Inference acceleration using low-power devices for human monitoring and security scenarios

Fuzzy Based Collaborative Task Offloading Scheme in the Densely Deployed Small-Cell Networks with Multi-Access Edge Computing

Contact Info

Product

Resources

About