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摘要

一个完整的人脸识别系统包括四个部分:人脸检测、图像预处理、特征提取和人脸识别。特征提取是人脸识别系统的关键步骤。如何有效地提取特征是一个非常重要的问题。在特征提取阶段,研究了PCA特征提取方法和2DPCA特征提取方法,并通过实验对两种方法进行了比较。由于采用2DPCA方法占用内存空间大,嵌入式系统资源有限,本文采用PCA特征提取方法。在人脸识别阶段,利用欧几里得距离计算每张人脸图像在人脸空间中的投影点,从而判断需要识别的人脸。
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Feature extraction and face recognition algorithm
A complete face recognition system includes four parts: face detection, image preprocessing, feature extraction and face recognition. Feature extraction is a key step in face recognition system. It is a very important problem how to extract features effectively. In the feature extraction phase, the PCA feature extraction method and 2DPCA feature extraction method are studied, and the two methods are compared by experiments. Since the 2DPCA method is used to account for a large memory space, and the embedded system resources are limited, this paper adopts the method of PCA feature extraction. In the face recognition stage, the Euclidean distance is used to calculate the projection points of each face image in the face space to judge which face to be recognized.
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