人脸特征提取与识别算法研究
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摘要
人脸识别是模式识别的一个重要领域和研究热点,它涉及面非常广。由于人脸图像受环境、表情等多种变化因素的影响,导致人脸识别研究复杂而艰巨,是一项极富挑战性的研究课题。目前,还有许多的问题和关键技术有待进一步解决和完善,其中主要包括：人脸特征提取阶段的完备性研究即如何充分考虑局部特征和全局特征、图像的平移/伸缩/旋转不变性等；分类识别阶段的识别性能研究即设计具有高精度识别率和快速分类的算法等。
本文以人脸识别为研究目的,重点研究了人脸特征提取和分类器设计的识别算法,并提出了一些新的特征提取方法和识别算法,在ORL、YALE人脸数据库上进行的大量实验分析论证表明,新方法在识别时间、识别率上获得了较好效果。
本论文的主要工作和贡献如下：
1)从识别时间和识别率两方面来考虑,用大量的实验分析讨论了小波变换在人脸识别中小波基的选择、分解层数的确定和分解系数的选择对它们的影响。
2)给出分块小波系数的概念,提出了一种基于分块小波的人脸识别算法以及基于分块小波的Curvelet变换人脸识别算法,利用了Curvelet变换图像的局部特征,改善了特征向量的稀疏化,利用PCA+LDA降维技术。在ORL和YALE人脸库上的实验结果表明相对传统的小波变换,新算法无论从识别率还是识别时间上都有了很大改进。特别是第二种算法在识别率至少提高了1个多百分点,在ORL人脸库上识别时间减少了近一半。
3)给出了基于小波域的Contourlet变换人脸识别新算法。Contourlet变换分别进行多尺度分析和方向分析,两种变换结合,克服小波变换方向选择性差,不适宜表示图像边缘、轮廓等线奇异性的结构特征的缺点,更有利于特征的提取和分析。
4)提出了特征四叉树的概念,给出了人脸特征四叉树的构建方法,达到了特征降维的作用；进一步结合特征四叉树向量的稀疏性和小波矩的旋转不变性,给出了稀疏小波矩的概念,提出了改进的基于特征四叉树的稀疏小波矩的人脸识别算法,并将其用于人脸识别,都取得了较好的试验结果。
5)对几种分类器进行了详细的分析及研究。给出了一种新的分类思想：类别特征法。给出了小波系数能量熵的概念,构建信息损失函数,以此作为随机森林的分类准则,首次提出了基于随机森林的人脸识别算法。充分利用几种分类器的优点,提出了两种新的组合分类器算法：a)KNN过滤的SVM算法：充分结合了K-近邻方法的快速分类能力及支持向量机在解决小样本问题上的优势。b)优化的融合分类器组合方法(KNN-类别特征法-SVM)：利用类别特征法先从整体上实现分类,然后对冗余信息利用KNN和SVM进行进一步分类识别。大量实验表明,新算法具有很好的识别能力。
Face identification is one of the important fields and hot research topics in pattern recognition. It involves a very wide area. Human face images are highly variable depending on influence factors such as circumstances and expressions, which make face identification a challenging and complex research task. Many problems and key technologies need to be further addressed and improved including the completeness of the feature extraction stage, that is, taking a full consideration of local and global features and image invariance under translation, stretching and rotation; the performance in the classification and identification stage with a high-precision recognition rate and fast classification algorithm.
The focus of this thesis is the recognition algorithm of the facial feature extraction and classifier design for human face identification. New methods of feature extraction and recognition algorithms of classification and recognition are proposed. The results based on the large number of experiments carried out on the ORL and YALE face database show that the new methods and recognition algorithms enhance the performance in the rate and speed of recognition.
The main work and contributions of this thesis are as follows:
1) Considering the rate and speed of the wavelet recognition algorithms, a large number of experiments have been carried out to compare the performance of face recognition with the different choice of wavelet bases, the determination of the number of decomposition levels, and the selection of decomposition coefficients.
2) The concept of block wavelet coefficients is given. A new face recognition algorithm base on the blocking wavelet transform is proposed. Combining the PCA+LDA methods, the algorithm reduced the characteristics dimension. Further more, in order to sparser eigenvector, this new face recognition algorithm is improved using the best local characteristics of curvelet transform. The experiments show that, compared with the traditional wavelet transform, the recognition rate is improved by at least more than1%on YALE, especially the recognition time is reduced by nearly50%on ORL.
3) A new face recognition algorithm of contourlet transform based on wavelet domain is proposed. Because of multi-scale analysis and direction analysis separately of contourlet transform, the combination of two transform can overcome the shortcomings of wavelet transform such as poor directional selectivity and unsuitable for describing the singularity structural characteristics such as the edge and the contour line of an image, so it is more conductive to extract and analyze the feature.
4) Inspired by the embedded zero-tree coding, the concept of quad-tree of characteristics, the method of constructing quad-tree of characteristics for face image are proposed, and the sparse wavelet moment is obtained by combining the sparsity of the characteristics quad-tree vector and the rotational invariance of the wavelet moment. Moreover, an improved sparse wavelet moment algorithm based on quad-tree of characteristics for face recognition is proposed. Experiments show the good performance of the new algorithm.
5) By the detailed analysis and study of the several classifiers. A new idea of classification named category characteristic method (CCM) is presented. The random forest has been put forward for the first time in face recognition. Two new classifier combination algorithms are proposed:firstly, SVM algorithm based on the KNN filter, secondly, optimal fusion classifier combination algorithm (KNN-CCM-SVM). A larger number of experiments show that the new algorithms have good ability to identify the face image.
本文以人脸识别为研究目的,重点研究了人脸特征提取和分类器设计的识别算法,并提出了一些新的特征提取方法和识别算法,在ORL、YALE人脸数据库上进行的大量实验分析论证表明,新方法在识别时间、识别率上获得了较好效果。
本论文的主要工作和贡献如下：
1)从识别时间和识别率两方面来考虑,用大量的实验分析讨论了小波变换在人脸识别中小波基的选择、分解层数的确定和分解系数的选择对它们的影响。
2)给出分块小波系数的概念,提出了一种基于分块小波的人脸识别算法以及基于分块小波的Curvelet变换人脸识别算法,利用了Curvelet变换图像的局部特征,改善了特征向量的稀疏化,利用PCA+LDA降维技术。在ORL和YALE人脸库上的实验结果表明相对传统的小波变换,新算法无论从识别率还是识别时间上都有了很大改进。特别是第二种算法在识别率至少提高了1个多百分点,在ORL人脸库上识别时间减少了近一半。
3)给出了基于小波域的Contourlet变换人脸识别新算法。Contourlet变换分别进行多尺度分析和方向分析,两种变换结合,克服小波变换方向选择性差,不适宜表示图像边缘、轮廓等线奇异性的结构特征的缺点,更有利于特征的提取和分析。
4)提出了特征四叉树的概念,给出了人脸特征四叉树的构建方法,达到了特征降维的作用；进一步结合特征四叉树向量的稀疏性和小波矩的旋转不变性,给出了稀疏小波矩的概念,提出了改进的基于特征四叉树的稀疏小波矩的人脸识别算法,并将其用于人脸识别,都取得了较好的试验结果。
5)对几种分类器进行了详细的分析及研究。给出了一种新的分类思想：类别特征法。给出了小波系数能量熵的概念,构建信息损失函数,以此作为随机森林的分类准则,首次提出了基于随机森林的人脸识别算法。充分利用几种分类器的优点,提出了两种新的组合分类器算法：a)KNN过滤的SVM算法：充分结合了K-近邻方法的快速分类能力及支持向量机在解决小样本问题上的优势。b)优化的融合分类器组合方法(KNN-类别特征法-SVM)：利用类别特征法先从整体上实现分类,然后对冗余信息利用KNN和SVM进行进一步分类识别。大量实验表明,新算法具有很好的识别能力。
Face identification is one of the important fields and hot research topics in pattern recognition. It involves a very wide area. Human face images are highly variable depending on influence factors such as circumstances and expressions, which make face identification a challenging and complex research task. Many problems and key technologies need to be further addressed and improved including the completeness of the feature extraction stage, that is, taking a full consideration of local and global features and image invariance under translation, stretching and rotation; the performance in the classification and identification stage with a high-precision recognition rate and fast classification algorithm.
The focus of this thesis is the recognition algorithm of the facial feature extraction and classifier design for human face identification. New methods of feature extraction and recognition algorithms of classification and recognition are proposed. The results based on the large number of experiments carried out on the ORL and YALE face database show that the new methods and recognition algorithms enhance the performance in the rate and speed of recognition.
The main work and contributions of this thesis are as follows:
1) Considering the rate and speed of the wavelet recognition algorithms, a large number of experiments have been carried out to compare the performance of face recognition with the different choice of wavelet bases, the determination of the number of decomposition levels, and the selection of decomposition coefficients.
2) The concept of block wavelet coefficients is given. A new face recognition algorithm base on the blocking wavelet transform is proposed. Combining the PCA+LDA methods, the algorithm reduced the characteristics dimension. Further more, in order to sparser eigenvector, this new face recognition algorithm is improved using the best local characteristics of curvelet transform. The experiments show that, compared with the traditional wavelet transform, the recognition rate is improved by at least more than1%on YALE, especially the recognition time is reduced by nearly50%on ORL.
3) A new face recognition algorithm of contourlet transform based on wavelet domain is proposed. Because of multi-scale analysis and direction analysis separately of contourlet transform, the combination of two transform can overcome the shortcomings of wavelet transform such as poor directional selectivity and unsuitable for describing the singularity structural characteristics such as the edge and the contour line of an image, so it is more conductive to extract and analyze the feature.
4) Inspired by the embedded zero-tree coding, the concept of quad-tree of characteristics, the method of constructing quad-tree of characteristics for face image are proposed, and the sparse wavelet moment is obtained by combining the sparsity of the characteristics quad-tree vector and the rotational invariance of the wavelet moment. Moreover, an improved sparse wavelet moment algorithm based on quad-tree of characteristics for face recognition is proposed. Experiments show the good performance of the new algorithm.
5) By the detailed analysis and study of the several classifiers. A new idea of classification named category characteristic method (CCM) is presented. The random forest has been put forward for the first time in face recognition. Two new classifier combination algorithms are proposed:firstly, SVM algorithm based on the KNN filter, secondly, optimal fusion classifier combination algorithm (KNN-CCM-SVM). A larger number of experiments show that the new algorithms have good ability to identify the face image.
引文
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