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Real time face recognition engine using compact features for electronics key

机译:使用紧凑型电子钥匙的实时面部识别引擎

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In this research, a design and implementation of real-time face recognition engine using compact features vector extracted by linear binary pattern (LBP) and zoned discrete cosine transforms (DCT) analysis is proposed for electronics key. The function of LBP is to normalize the lighting variations of the input face image and the function of zoned DCT is to define the local descriptors of the face image. In order to get more compact features vector, the predictive LDA is employed for dimensional reduction. The aims of this research is to develop fast and strong face recognition that can be implemented for electronic key which will be implemented for substituting current security system (PIN and password). In addition, the recognition engine is also designed for real time face recognition which can work on hardware having limited resources such as Intel atom computer, raspberry pi, and android smart phone. The experimental results show that the proposed engine provide high enough recognition rate, and small false rejection rate (FRR) and false acceptance rate (FAR). In addition, the engine needs short processing time.
机译:在本研究中,提出了用线性二进制图案(LBP)提取的紧凑型特征向量和分区离散余弦变换(DCT)分析的实时面部识别引擎的设计和实现。 LBP的功能是归一化输入面部图像的照明变化和分区DCT的功能是定义面部图像的本地描述符。为了获得更紧凑的特征向量,预测LDA用于尺寸减少。该研究的目的是开发快速且强大的面部识别,可以为电子钥匙实施,这将被实施为代替当前的安全系统(PIN和密码)。此外,识别引擎还设计用于实时面部识别,可以在具有有限资源的硬件上工作,例如英特尔原子计算机,覆盆子PI和Android智能手机。实验结果表明,该发动机提供足够高的识别率,小幅抑制率(FRR)和虚假接受率(远)。此外,发动机需要短暂的处理时间。

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