英国论文查重:人脸识别系统的结构

英国论文查重:人脸识别系统的结构

由上表可知,人脸识别的第一步是对图像进行捕获,该图像通过人脸识别的不同步骤进一步移动。人脸检测是一个过程,其主要功能是从捕获的图像或从数据库中选择的图像中检测人脸。人脸检测过程主要是识别捕获图像是否具有人脸。当该过程识别出图像具有人脸时,输出被发送到预处理步骤(Zhou, 2013)。人脸检测和人脸识别是有区别的。人脸检测是检测图像中的人脸的过程,而人脸识别是识别图像中的个体的过程。它们都是专注于面部信息不同方面的独特视觉路径。人脸识别是关于身份的。人脸检测的重点是识别图像或数字图像中的至少一个人脸,而人脸识别是“确定并存储在至少一个数字图像中至少一个被检测人脸的位置的区域坐标”(Ganong et al., 2017, p. 43)。

英国论文查重:人脸识别系统的结构

当前的人脸识别问题是通过深度学习和神经网络来解决的。神经网络被认为是一种非常有效和稳健的技术,可以帮助产生已知的数据和未知的数据。该技术对线性和非线性可分离数据集有效(Sun et al., 2015)。人脸识别问题通过强神经网络来解决,强神经网络包括人工神经元网络,称为“节点”。为了解决人脸识别问题,首先对人脸图像进行预处理,进一步包括人脸检测、人脸跟踪、人脸裁剪和人脸对齐(Xinhua and Yu, 2015)。神经网络的节点相互连接,并将该连接的强度赋值。如果该连接的值很高,则认为该连接是强连接。

英国论文查重:人脸识别系统的结构

According to the above figure, the first step of face recognition is capturing the image and this image further moves through different steps of face recognition. Face detection is the process whose main function is to detect the face from the captured image, or from the image that is selected from the database. The face detection process mainly works towards indentifying that the capture image has the face or not. When this process identifies that the image has a face then output is sent to pre-processing step (Zhou, 2013). There is a difference between face detection and face recognition. Face detection is the process of detecting a face in the image, while face recognition is the process of recognizing the individual in the image. Both of them are the distinct visual pathways that focus on different aspects of facial information. Face recognition is about the identity. Face detection focuses on identifying at least one face in the image or the digital image, while face recognition is to “determine and store area co-ordinates of a location of the at least one detected face in the at least one digital image” (Ganong et al., 2017, p. 43).

英国论文查重:人脸识别系统的结构

The contemporary face recognition problems are addressed by deep learning and neural networks. Neural network is considered as the very effective and robust technique that can help in producing the known data as well as the unknown data. This technique works effectively for the linear and non-linear separable dataset (Sun et al., 2015). The problem of face recognition is solved through the strong neural network that includes the network of artificial neurons, which are known as “nodes”. For solving the problem of face recognition, firstly, the face image pre-processing is done and it further includes face detection, face tracking, face cropping and face alignment (Xinhua and Yu, 2015). The nodes of the neural network are connected to each other, and strength of this connection is assigned with value. If the value of this connection is produced to be high, then connection is considered to be strong.