本文的主要內容是人臉識別，從上圖可以看出，人臉識別的第一步是捕捉圖像，這張圖像通過人臉識別的不同步驟進一步移動。人臉檢測是一個過程，其主要功能是從採集的圖像或從數據庫中選擇的圖像中檢測人臉。人臉檢測過程主要是識別捕捉到的圖像是否具有人臉。當這個過程識別出圖像具有人臉時，輸出被發送到預處理步驟(Zhou, 2013)。人臉檢測和人臉識別是有區別的。本篇論文 代寫文章由英國論文通AssignmentPass輔導網整理，供大家參考閱讀。
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).
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.
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