Embedded Face Recognition System Based on Multi-task Convolutional Neural Network and LBP Features

Zhang, Mengyue; Liao, Weihan; Zhang, Jianlian; Gao, Haichun; Wang, Fanyi; Lin, Bin

doi:10.1109/iciase45644.2019.9074104

Cited by 6 publications

(2 citation statements)

References 6 publications

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“…Finally, develop a unified framework to combine these two components which achieve the competitive performance on the challenging FDDB and WIDER FACE face detection benchmarks while keeping real time performance. In [2], The experiments illustrate that face recognition systems based on MTCNN and LBP features can achieve high recognition rate on embedded devices in limited data. Neural network greatly optimizes the accuracy and stability of the system.…”

Section: Methodologiesmentioning

confidence: 99%

Facial Recognition Technology for Identifying Missing Individuals and Wanted Criminals

et al. 2024

IJFMR

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Fast and accurate user identification and verification is always desirable. Face recognition, which is machine recognition of a person's face by analyzing patterns on facial features, is becoming important for security and validation. Less interaction from users contributes high enrolment as well as easily applicable for current technology further adds its importance. In this regard, we propose a Multi-tasked Convolutional Neural Network (CNN) based face recognition technique previously done with eigenfaces but CNN has better accuracy. This project proposes to use this technology for identifying criminals who are on the run from their previous records. An NCRB (National Crime Records Bureau) report shows that 70% of crimes are repeatedly committed by the same criminals. These criminals can be identified by the face recognition from an image or video frame which is captured by the cameras which are installed in various locations and it can also be used for identifying missing children. This system will decrease crimes and ensure security in our society.

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Section: Methodologiesmentioning

confidence: 99%

Facial Recognition Technology for Identifying Missing Individuals and Wanted Criminals

et al. 2024

IJFMR

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“…By including the AI computing in the edge, the IoT can swiftly perform the AI operation and significantly reduce the workload of the system core on the edge. For the AI edge as an embedded system, it is essential that the computation module should be realized with restricted resources as well as operated with low power [27][28][29][30][31][32][33]. Therefore, analyzing the algorithms and neurons which occupy the resources of the AI processor and applying the processor to embedded AI system according to applications are important.…”

Section: Introductionmentioning

confidence: 99%

Intellino: Processor for Embedded Artificial Intelligence

et al. 2020

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The development of computation technology and artificial intelligence (AI) field brings about AI to be applied to various system. In addition, the research on hardware-based AI processors leads to the minimization of AI devices. By adapting the AI device to the edge of internet of things (IoT), the system can perform AI operation promptly on the edge and reduce the workload of the system core. As the edge is influenced by the characteristics of the embedded system, implementing hardware which operates with low power in restricted resources on a processor is necessary. In this paper, we propose the intellino, a processor for embedded artificial intelligence. Intellino ensures low power operation based on optimized AI algorithms and reduces the workload of the system core through the hardware implementation of a neural network. In addition, intellino’s dedicated protocol helps the embedded system to enhance the performance. We measure intellino performance, achieving over 95% accuracy, and verify our proposal with an field programmable gate array (FPGA) prototyping.

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CFSM: a novel frame analyzing mechanism for real-time face recognition system on the embedded system

Chu

Chen

Zheng

2021

Multimed Tools Appl

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Embedded Face Recognition System Based on Multi-task Convolutional Neural Network and LBP Features

Cited by 6 publications

References 6 publications

Facial Recognition Technology for Identifying Missing Individuals and Wanted Criminals

Facial Recognition Technology for Identifying Missing Individuals and Wanted Criminals

Intellino: Processor for Embedded Artificial Intelligence

CFSM: a novel frame analyzing mechanism for real-time face recognition system on the embedded system

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