【正文】 科學技術 和生物醫(yī)學 的發(fā)展使得利用生物特征 識別成為了可能。 本文 研究的就是基于 PCA 的人臉識別算法的實現(xiàn) 。 接下來是人臉圖像預處理方法。 【關鍵詞】 人臉識別 PCA 算法 奇異值分解定理 歐幾里得距離 III ABSTRACT With the development of science and technology, the progress of human society, the traditional identification is easy to lose, easy to be cracked and it has not play an identifiable role. People need a more secure and reliable identification technology. Biometric is unique, easy to lose and replication characteristics of good meet the needs of the identification. With the development of puter science and technology and biomedical makes use of biometric identification has bee possible. In the field of biometric identification, face recognition with the advantages of operation is fast and simple, the results are intuitive, accurate and reliable,do not need coordination, has bee the focus of attention. The principal ponent analysis (PCA) to extract high dimensional face image of the main element, making the images are processed in lowdimensional space and it reduces the difficulty of image processing. PCA solves effectively the problem of high dimension image space and it has bee a very important theory in face recognition field. This paper is in this context of writing from. In accordance with the full recognition process to analyze the performance of PCAbased face recognition algorithm. The first to use the method of access to monly used face images for face images. In order to better analysis is based on the performance of the PCA face recognition system selected Essex face database. Next is the face image preprocessing methods. Essex face image quality is better, and have done the appropriate pretreatment, using only grayscale processing of this trial. Then use the PCA for face feature extraction using singular value deposition theorem to calculate the covariance matrix of the eigenvalues and eigenvectors, and use the Euclidean distance of the nearest neighbor classifier to the classification of human face discrimination. In the experiment, we found that a high recognition rate of the PCAbased face recognition system, but with a certain robustness, the PCAbased face recognition algorithm to achieve meaningful. 【 Key words】 face recognition PCA algorithm SVD Euclidean distance IV 目 錄 前 言 ................................................................. 1 第一章 人臉識別系統(tǒng)概述 ............................................... 2 第一節(jié) 人臉識別的研究概況 .......................................... 2 第二節(jié) 人臉識別的發(fā)展趨勢 .......................................... 3 一、多數(shù)據融合與方法綜合 ........................................ 4 二、動態(tài)跟蹤人臉識別系統(tǒng) ........................................ 4 三、基于小波神經網絡的人臉識別 .................................. 4 四、三維人臉識別 ................................................ 4 五、適應各種復雜背景的人臉分割技術 .............................. 4 六、全自動人臉識別技術 .......................................... 4 第三節(jié) 人臉識別技術的主要難點 ...................................... 5 一、復雜條件下人臉的檢測和關鍵點定位 ............................ 5 二、光照問題 .................................................... 5 三、資態(tài)問題 .................................................... 5 四、表情問題 .................................................... 5 五、遮擋問題 .................................................... 5 第四節(jié) 人臉識別流程 ................................................ 6 一、人臉圖像采集 ................................................ 6 二、預處理 ...................................................... 6 三、特征提取 .................................................... 6 第五節(jié) 本章小結 .................................................... 8 第二章 人臉圖 像的獲取 ................................................. 9 第一節(jié) 人臉圖像獲取 ................................................ 9 第二節(jié) 人臉分割 .................................................... 9 第三節(jié) 人臉數(shù)據庫 ................................................. 10 第四節(jié) 本章小結 ................................................... 11 第三章 人臉圖像的預處理 .............................................. 12 第一節(jié) 人臉圖像格式 ............................................... 12 一、 JPEG 格式 ................................................... 12 二、 JPEG2021 格式 ............................................... 12 V 三、 BMP 格式 .................................................... 13 四、 GIF 格式 .................................................... 13 五、 PNG 格式 .................................................... 14 第二節(jié) 人臉圖像常用預處理方法 ..................................... 14 一、灰度變化 ................................................... 14 二、二值化 ..................................................... 15 三、直方圖均衡 ................................................. 15 四、圖像濾波 ................................................... 16 五、圖像銳化 ................................................... 17 六、圖像歸一化 ................................................. 18 第三節(jié) 本章小結 ................................................... 19 第四章 人臉識別 ...................................................... 20 第一節(jié) 主成分分析基本理論 ......................................... 20 一、什么是主成分分析？ ......................................... 20 二、例子 ....................................................... 20 三、基變換 ..................................................... 21 四、方差 ....................................................... 24 五、 PCA 求解：特征根分解 ........................................ 27 六、 PCA 的假設 .................................................. 28 七、總結： ..................................................... 29 八、在計算機視覺領域的應用 ..................................... 31 第二節(jié) 基于 PCA 人 臉識別算法的實現(xiàn) ................................. 32 一、創(chuàng)建數(shù)據庫 ................................................. 32 二、計算特征臉 ................................................. 33 三、人臉識別 ................................................... 35 第三節(jié) 本章小結 ...........................