基于主成分分析(PCA)方法的智能家居安全图像处理技术

Rai Purnama Rizki, E. A. Z. Hamidi, L. Kamelia, R. Sururie
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引用次数: 1

摘要

智能家居是普适计算科学分支的一个应用。智能家居分为三类,即舒适、医疗和安全。安全系统是智能家居技术的一部分,因为犯罪的强度在增加,特别是在住宅区,这是非常重要的。如果用户输入正确的密码,系统将通过网络摄像头检测人脸。人脸识别将由树莓派3微控制器处理,采用主成分分析方法,使用OpenCV和Python软件,该软件具有输出,即电磁门锁和蜂鸣器形式的执行器。测试结果表明,当密码输入成功时,摄像头可以进行人脸检测,当数据库与摄像头采集的数据或测试数据不匹配时,蜂鸣器执行器打开,当数据库与传感器采集的测试数据匹配时,电磁门锁执行器可以运行。摄像头。人脸检测的平均响应时间为1.35秒。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image Processing Technique for Smart Home Security Based On the Principal Component Analysis (PCA) Methods
Smart home is one application of the pervasive computing branch of science. Three categories of smart homes, namely comfort, healthcare, and security. The security system is a part of smart home technology that is very important because the intensity of crime is increasing, especially in residential areas. The system will detect the face by the webcam camera if the user enters the correct password. Face recognition will be processed by the Raspberry pi 3 microcontroller with the Principal Component Analysis method using OpenCV and Python software which has outputs, namely actuators in the form of a solenoid lock door and buzzer. The test results show that the webcam can perform face detection when the password input is successful, then the buzzer actuator can turn on when the database does not match the data taken by the webcam or the test data and the solenoid door lock actuator can run if the database matches the test data taken by the sensor. webcam. The mean response time of face detection is 1.35 seconds.
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