一种低成本的嵌入式人脸检测与识别系统

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI:10.1109/ColCACI50549.2020.9247856

R. Sandoval, Vanessa Camino, Ricardo Flores Moyano, Daniel Riofrío, Noel Pérez, D. Benítez

{"title":"一种低成本的嵌入式人脸检测与识别系统","authors":"R. Sandoval, Vanessa Camino, Ricardo Flores Moyano, Daniel Riofrío, Noel Pérez, D. Benítez","doi":"10.1109/ColCACI50549.2020.9247856","DOIUrl":null,"url":null,"abstract":"This paper explores the feasibility of using commercially available off-the-shelf components to implement a low-cost embedded system as the core of a facial detection and recognition system. The system is composed of a Raspberry Pi camera module and a Raspberry Pi B+ enhanced by an Intel Neural Compute Stick 2. Four supervised learning models were implemented on the embedded system for face recognition under different conditions to determine the limitations and capabilities of the system, and the best operational conditions. Best results were achieved when using a Multilayer Perceptron (MLP) algorithm and the distance of the subject to the camera was between 0.3 to 1 meters, the illumination factor in the range from 115 to 130 lux and the horizontal face rotation between -5° to +5°.","PeriodicalId":446750,"journal":{"name":"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"On the Use of a Low-Cost Embedded System for Face Detection and Recognition\",\"authors\":\"R. Sandoval, Vanessa Camino, Ricardo Flores Moyano, Daniel Riofrío, Noel Pérez, D. Benítez\",\"doi\":\"10.1109/ColCACI50549.2020.9247856\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper explores the feasibility of using commercially available off-the-shelf components to implement a low-cost embedded system as the core of a facial detection and recognition system. The system is composed of a Raspberry Pi camera module and a Raspberry Pi B+ enhanced by an Intel Neural Compute Stick 2. Four supervised learning models were implemented on the embedded system for face recognition under different conditions to determine the limitations and capabilities of the system, and the best operational conditions. Best results were achieved when using a Multilayer Perceptron (MLP) algorithm and the distance of the subject to the camera was between 0.3 to 1 meters, the illumination factor in the range from 115 to 130 lux and the horizontal face rotation between -5° to +5°.\",\"PeriodicalId\":446750,\"journal\":{\"name\":\"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ColCACI50549.2020.9247856\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ColCACI50549.2020.9247856","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 2

摘要

本文探讨了利用市售现成组件实现低成本嵌入式系统作为人脸检测与识别系统核心的可行性。该系统由树莓派相机模块和树莓派B+组成，该B+由英特尔神经计算棒2增强。在不同条件下对嵌入式系统的人脸识别进行了四种监督学习模型的实现，以确定系统的局限性和能力，以及最佳运行条件。使用多层感知器(Multilayer Perceptron, MLP)算法，受试者与相机的距离在0.3 ~ 1米之间，光照因子在115 ~ 130勒克斯之间，水平面部旋转在-5°~ +5°之间时，可以获得最佳效果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

On the Use of a Low-Cost Embedded System for Face Detection and Recognition

This paper explores the feasibility of using commercially available off-the-shelf components to implement a low-cost embedded system as the core of a facial detection and recognition system. The system is composed of a Raspberry Pi camera module and a Raspberry Pi B+ enhanced by an Intel Neural Compute Stick 2. Four supervised learning models were implemented on the embedded system for face recognition under different conditions to determine the limitations and capabilities of the system, and the best operational conditions. Best results were achieved when using a Multilayer Perceptron (MLP) algorithm and the distance of the subject to the camera was between 0.3 to 1 meters, the illumination factor in the range from 115 to 130 lux and the horizontal face rotation between -5° to +5°.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)

自引率

0.00%

发文量