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Image Processing Technique for Smart Home Security Based On the Principal Component Analysis (PCA) Methods

机译:基于主成分分析(PCA)方法的智能家居安防图像处理技术

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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.
机译:智能家居是科学计算领域的一种应用。智能家居分为三类,即舒适性,医疗保健和安全性。安全系统是智能家居技术的一部分,这非常重要,因为犯罪的强度正在增加,尤其是在居民区。如果用户输入正确的密码,系统将通过网络摄像头检测面部。 Raspberry pi 3微控制器将使用OpenCV和Python软件通过主成分分析方法处理人脸识别,该软件具有输出,即电磁锁门和蜂鸣器形式的执行器。测试结果表明,密码输入成功后,网络摄像头可以进行人脸检测;当数据库与网络摄像头获取的数据或测试数据不匹配时,蜂鸣器执行器可以开启;如果出现以下情况,电磁门锁执行器可以运行:数据库匹配传感器获取的测试数据。摄像头。人脸检测的平均响应时间为1.35秒。

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