分類號密級

UDC 


學位論
文


人臉檢測和識別算法的研究與實
現




東北大學信息科學與工程
申請學位級別
： 
博士學科類別：工學
學科專業名稱
： 
模式識別與智能系統
論文提交日期：2006 年1 月10 日論文答辯日期：2006 年2 月22 日
學位授予日期：答辯委員會主席：
評閱人：

東北大學
2006 年1 月


A Dissertation for the Degree of Doctor in Pattern Recognition and 
Intelligent System 

Research and Realization in Face Detection 
and Recognition Algorithms


By Lihong Zhao 

Supervisor：Professor Xinhe Xu 

Northeastern University
January 2006



獨創聲
明


本人聲明所呈交的學位論文是在導師的指導下完成的。論文中取得的研究成果除加
以標注和致謝的地方外，不包含其他人已經發表或撰寫過的研究成果，也不包括本人為
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東北大學博士學位論文摘要

人臉檢測和識別算法的研究與實現

摘
要


生物特征識別是利用人類特有的生理或行為特征來識別個人身份的技術，它提供了
一種高可靠性、高穩定性的身份鑒別途徑。人臉檢測和識別是目前生物特征識別中最受
人們關注的一個分支，是當前圖像處理、模式識別和計算機視覺領域內的一個熱門研究
課題，在公安部門罪犯搜索、安全部門動態監視識別、銀行密碼系統等許多領域有廣泛
的應用。與指紋、視網膜、虹膜、掌紋等其他人體生物特征識別方法相比，人臉識別具
有直接、友好，使用者無心理障礙等特點。本文對此進行了較為深入的研究，論文的主
要工作和成果有以下幾個方面：

⑴.. 全面概述了生物特征識別技術及其發展方向、應用背景和研究意義，重點描述
了人臉識別技術的研究內容、方法、應用前景，介紹了人臉識別技術在國內外的研究現
狀，對人臉自動檢測與識別技術進行了綜述。
⑵.. 提出一種非線性變換的彩色空間來描述膚色模型，在該非線性彩色空間上進行
人臉膚色的分割，采用基于區域增長算法的自適應閾值處理，實現了一個完整的皮膚分
類器。通過使用自適應閾值的模糊分割技術，使皮膚區域與非皮膚區域有效地分割開，
從而得到人臉候選區域。提出利用多尺度形態邊緣檢測算法定位眼睛和嘴的位置，根據
均值和方差分割出的紋理特征和人臉幾何特性來定位人臉，從而驗證候選區域是否為人
臉。
⑶.. 在標準PCA 原理基礎上，分別提出了對稱主成分分析和核主成分分析算法進行
人臉識別。通過引入鏡像樣本，將人臉圖像進行奇偶分解，并分別對奇偶圖像應用KL 
展開，提取奇偶對稱KL 特征；根據各個特征分量在人臉中所占能量比例的不同以及對
視角、旋轉、光照等干擾的不同敏感程度，進行特征選擇，增強特征的穩定性；從理論
分析入手，建立理論基礎，并將該算法成功應用于人臉識別中。該算法從理論上提出奇
偶正交重構，在應用上利用鏡像樣本擴大樣本容量，提高了識別性能并增強了人臉識別
算法的實用性。作為一類核方法，KPCA 方法在模式識別領域中得到了較多的應用，其
基礎是使用KPCA 進行特征抽取。在進行非線性映射之前，首先利用經典的主分量分析
降維，然后再進行核主分量分析(KPCA)。在ORL 標準人臉庫上的實驗結果驗證了所提
算法的有效性。
⑷.. 提出了基于小波變換圖像相關性的人臉識別方法。用小波變換將原始圖像分解
提取特征，可以有效地降低特征向量的維數；將訓練集中的5 幅圖像取平均值作為模板
臉，計算測試集中的5 幅小波變換圖像與模板臉的相關系數，并進行比較。在ORL 人
臉庫上的實驗結果表明，提出的方法可以達到98.5% 的正確識別率，計算量小，速度快，
可用于各種人臉識別系統中。
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東北大學博士學位論文摘要

⑸.. 提出融合小波特征和離散余弦變換特征的支持向量機人臉識別方法。通過小波
變換提取圖像的低頻分量，再利用離散余弦變換的較好壓縮性能及計算的有效性提取樣
本圖像的特征，該方法提取的特征少而精，使輸入向量的維數大大減小，減少了計算的
復雜性；同時結合支持向量機的強大分類能力，對標準的ORL 人臉庫進行分類識別，
取得了很好的分類、識別效果。
⑹.. 提出將小波變換和主成分分析方法結合提取人臉特征，有效地減少了人臉圖像
維數，減少了神經元網絡的訓練和識別時間，提高了效率；利用隱層數和隱層單元數計
算公式，合理選擇神經元網絡隱層數和隱層單元數，獲得較好的識別結果。
由于人臉自動識別系統相對比較復雜，涉及的內容很多，本文雖然在人臉檢測與識
別方面取得了一些成果，但距離實際應用還有一定的差距，有待于在今后的工作中繼續
研究改進和完善。人臉自動識別是近年來非常活躍的研究領域，新思想、新技術、新方
法和新應用層出不窮，相信在不久的將來一定會找到比較完美的解決辦法，到那時候人
們就可以更加充分的享受這一技術給人們的工作和生活帶來的方便。

關鍵詞：生物特征識別；人臉檢測；人臉識別；小波變換；離散余弦變換；支持向量機；
主成分分析；鏡像主成分分析；核主成分分析；特征提取和選擇；神經元網絡

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東北大學博士學位論文Abstract 

Research and Realization in Face Detection and Recognition
Algorithms


Abstract 

Biometrics is a kind of science and technology using individual physiological or 
behavioral characteristics to verify identity. It provides a highly reliable and robust approach 
to the identity recognition. Automatic face detection and recognition is one of the most 
attention branches of biometrics and it is also the one of the most active and challenging tasks 
for image processing, pattern recognition and computer vision. It is widely applied in 
commercial and law area, such as mug shots retrieval, real-time video surveillance in security 
system and cryptography in bank and so on. Face recognition has direct, friendly 
characteristic s and it is no psychological obstacle for users. This dissertation mainly studies 
the approaches to frontal face detection and recognition. The main research works and 
contributions are as the following. 

⑴The biometrics technology and its development, application, and signification is 
summarized. The research content, approach and development are emphasized. The research 
status is introduced. The technology of the face detection and recognition are summarized. 

⑵A color space based on non-linear transformation is proposed. The face skin 
segmentation is finished in this non-linear color space. The face skin classification based on 
algorithms of adaptive threshold of region growing is realized. The skin regions and non-skin 
regions are separated with the fuzzy segmentation of adaptive threshold. The eyes and mouth 
are located with the multi-scale morphological algorithms. The face is located by the texture 
and geometrical features of face, then it is tested whether the candidate region is the face or 
not. 

⑶Symmetrical Principal Component Analysis (SPCA) and Kernel Principal Component 
Analysis (KPCA) are proposed based on the Classical Principal Component Analysis (CPCA). 
The face image is decomposed to odd and even images by introducing the mirror example to 
extract the odd and even symmetrical Karhunen-Loeve features. The features are select based 
on the different proportion of feature component in the face image and the different 
sensitivities in visual angle, rotation and illumination. The odd and even orthonormal 
reconstructure in the algorithm is proposed theoretically and higher correct. Recognition rate 
is achieved for the face with SPCA. Its main idea is that CPCA is first employed to preprocess 
the original training images before the nonlinear mapping and KPCA is used to extract 
features. The experimental results on ORL face databases indicate that the proposed method is 
more effective. 

⑷The paper proposes a classification method based on wavelet transform and features 
correlation. Its main idea is that the wavelet transform is first employed to preprocess the 

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東北大學博士學位論文Abstract 

original face image and reduced the dimensions of the feature space. The mean of five face 
images in training set is taken as a template faces. The correlationcoefficient is calculated and 
compared with five images of testing set and template face. The experimental results on ORL 
face databases indicate that the proposed method is more effective and the correct recognition 
rate is 98.5%. The approach is simple and faster and retains its accuracy. It is verified that 
the proposed algorithm is effective in the different application systems of face recognition. 

⑸A method of face recognition based on wavelet transform and Discrete Cosine 
Transform (DCT) and SVM is proposed. The low frequency sub-image is transformed by 
DCT, and only a small set of coefficients is retained as the features that are inputted to SVM. 
The experiments show that the performance is satisfactory. 

⑹A method of face recognition based on wavelet transform and principal component 
analysis is proposed to extract feature and reduce the dimensional feature space. The reduced 
features are inputted into BP neural network. Applying optimum algorithm of neural network 
and algebra equation theory and hidden structure basis equation, direct and indirect computing 
methods are studied to compute quantitatively the numbers of hidden layers and the unit 
numbers per hidden layer. The experiments show that the performance is perfect. 

Automatic face recognition system is comparatively complicated and involves a large 
number of contents. Although some achievement has been gained in this dissertation, more 
research is still needed to transmit the theory into practical application. More research work is 
needed to improve and perfect the methods in the future. With long term researching and 
studying, the era of intelligent acquisition and processing and application for face recognition 
will come true. 

Key words ：Biometrics, Face detection, Face recognition, Wavelet transform, Discrete Cosine 
Transform (DCT), Support Vector Machine(SVM), Principal Component Analysis(SPCA), 
Symmetrical Principal Component Analysis(SPCA), Kernel Principal Component 
Analysis(KPCA), Feature extract and select, Neural network. 

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東北大學博士學位論文第一章緒論

目
錄


獨創聲明.......................................................................................................
I
摘要..........................................................................................................II
Abstract ...................................................................................................... IV
目錄........................................................................................................ VI