Face Recognition on Bag Locking Mechanism

M. Ishak, N. H. Idris, S. Sahlan
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引用次数: 1

Abstract

With the emergent of biometric technology, people are no longer afraid to keep their important things in the safe box or room or even facility. This is because; human beings have unique features that distinguish them with other people. The scheme is based on an information theory approach that decomposes face images into a small set of characteristic feature images called ‘Eigenfaces’, which are actually the principal components of the initial training set of face images. In this report, thorough explanation on design process of face recognition on bags locking mechanism will be elucidated. The results and analysis of the proposed design prototype also presented and explained. The platform for executing the algorithm is on the Raspberry Pi. There are two artificial intelligent techniques applied to manipulate and processing data which is fuzzy logic and neural networks. Both systems are interdependent with each other, so that it can calculate and analyse data precisely. The receive image from the camera is analysed through the Eigenfaces algorithm. The algorithm is using Principal Component Analysis (PCA) method which comprise of artificial neural network paradigm and also statistical paradigm.
锁袋机构的人脸识别
随着生物识别技术的出现,人们不再害怕把重要的东西放在保险箱、房间甚至设施里。这是因为;人类有独特的特征,使他们与其他人区别开来。该方案基于信息论方法,将人脸图像分解为一组称为“特征脸”的小特征图像,这些特征图像实际上是人脸图像初始训练集的主成分。在本报告中,将对人脸识别锁袋机构的设计过程进行深入的阐述。并对所提出的设计原型的结果和分析进行了介绍和说明。执行该算法的平台是在树莓派上。模糊逻辑和神经网络是用于数据处理和处理的两种人工智能技术。这两个系统是相互依赖的,因此它可以精确地计算和分析数据。通过特征面算法对摄像机接收到的图像进行分析。该算法采用主成分分析(PCA)方法,该方法由人工神经网络范式和统计范式组成。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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