【正文】 main research works and contributions are as the following. First, the research content, approach and development are emphasized. The research status is introduced. The technology of the face detection and recognition are summarized. And the paper describes face preprocessing in detail which is and important step in the face recognition. The face preprocessing methods we adopt are based on image processing techniques. The main purpose is to get the standardized facial images, and to eliminate the impact of illumination to some extent. In this paper, several key preprocessing methods are introduced, such as geometry normalization, grayscale normalization and images binaryconversion. Principal Component Analysis (PCA) face recognition methods as the foundation of the KL transformation is the most superior in the image pression .By using PCA, the dimension of the input is reduced while the main ponents are maintained. The major idea of PCA is to depose a data space into a linear bination of a small collection of the III facerecognition literature, the eigenvectors can be referred to as eigenfaces. The probe is identified by first projection to all gallery images. We denote a probe .A probe is paring the projection to all gallery images, and it causes around the pression the mean error to be youngest. But in the PCAbased face recognition technique, the 2D face image matrices must be previously transformed into 1 D image vectors. The resulting image vectors of faces usually lead to a high dimensional image vector space, where it is difficult to evaluate the covariance matrix accurately due to its large size and the relatively small number of training samples. Key words Face recognition ； Face pretreatment； PCA 目 錄 IV 第一章 緒 論 ................................................................................................ 1 ............................................................................. 1 ............................................................... 3 ........................................................................................... 6 ............................................................................... 6 .......................................................................... 6 ........................................................ 7 KL 變換的特征臉?lè)椒? ............................................................ 9 ..................................................................................... 10 .............................................................. 12 ......................................................... 13 FISHER 線性判別式的方法 .................................................. 13 ................................................................... 14 ............................................................................................ 14 ....................................................... 15 ................................................................... 15 ....................................................................... 17 第二章人臉圖像預(yù)處理 .............................................................................. 18 .................................................................................................................... 18 ............................................................................................ 18 .............................................................................. 19 ............................................................................ 19 V ....................................................................... 20 .......................................................................................................... 23 第三章 基于 PCA的人臉識(shí)別方法 ............................................................. 23 .................................................................................................................... 23 PCA 人臉識(shí)別方法原理 .............................................................................. 23 ................................................................................. 24 KL 變換的原理 ....................................................................... 24 ..................................................................................... 26 ..........................................................27 PCA 人臉識(shí)別 ................................................................................... 28 .......................................................................................... 28 PCA 人臉識(shí)別方法的實(shí)現(xiàn)過(guò)程 ........................................... 29 ............................................................................................... 30 ............................................................................................... 32 第四章 實(shí)驗(yàn)過(guò)程顯示及分析 .................................................................... 33 引言 ................................................................................................................... 33 實(shí)驗(yàn)過(guò)程 ......................................................................................................... 33 致 謝 ......................................................................................................... 37 參考 文獻(xiàn) ..................................................................................................... 38 附錄 ............................................................................................................. 39 1 第一章 緒 論 人臉識(shí)別研究的目的意義 隨著信息技術(shù)及網(wǎng)絡(luò)的高速發(fā)展，人們的生活及身份日益數(shù)字化，信息的安全性和隱蔽性越來(lái)越受到人們的重視，身份識(shí)別與認(rèn)證技術(shù)也因此得到了較快的發(fā)展。這種方法使得壓縮前后的均方誤差最小，且變換后的低維空間有很好的分辨能力。 其次，本文重點(diǎn)描述了人臉識(shí)別的經(jīng)典方法， PCA方法。人臉檢測(cè)和識(shí)別是目前生物特征識(shí)別中最受人們關(guān)注的一個(gè)分支，是當(dāng)前圖像處理、模式識(shí)別和計(jì)算機(jī)視覺(jué)領(lǐng)