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Accuracy Comparison of Home Security Face Recognition Model in The Several Lighting Condition Using Some Kinect Produced Image

机译:一些kinect产生图像的几个照明条件中家庭安全面部识别模型的准确性比较

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As technology advances, security systems and the development of artificial intelligence systems around the world continue to be developed. Artificial intelligence is very often used to automate parts of a system. In terms of data security and physical security, the use of artificial intelligence to be able to recognize a person’s face plays a very important role in increasing the efficiency and management of knowledge. This paper was written with the aim of proposing a comprehensive comparison of accuracy, in recognizing faces using the features found on the Kinect camera (depth images, 3-dimensional images, and infrared images), compared to features found on ordinary cameras (RGB images), using techniques digital image processing in several different light conditions (dark room conditions and bright / standard room lighting). This research was conducted by comparing how reliably the facial recognition system classifies images that are part of the data class, as well as fake class images in the home security system. From this research, the Support Vector Machine (SVM) algorithm is used for classification, resulting in 20% better accuracy for images produced by Kinect cameras than for standard RGB cameras.
机译:随着技术的进步,安全系统和世界各地的人工智能系统的发展仍然发展。人工智能通常用于自动化系统的部分。在数据安全和物理安全方面,使用人工智能能够识别一个人的脸在提高知识效率和管理方面发挥着非常重要的作用。本文是旨在提出完整比较准确性的旨在使用Kinect摄像机(深度图像,三维图像和红外图像)的特征来识别面,与普通相机上的特征相比(RGB图像),在几种不同的光线条件下使用技术数字图像处理(暗室条件和明亮/标准房照明)。通过比较面部识别系统对属于数据类的一部分的图像以及家庭安全系统中的假类图像进行比较来进行该研究。从本研究中,支持向量机(SVM)算法用于分类,导致由Kinect摄像机生产的图像比标准RGB相机产生的图像的更好精度为20%。

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