人脸识别中图像特征提取与匹配技术研究
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摘要
伴随信息技术、人工智能、模式识别、计算机视觉等新技术的快速发展,人脸识别已被广泛应用在公共安全、信息安全、金融等领域,近几年已成为图像处理等领域的研究热点课题之一。对于一个人脸识别系统,图像匹配的目的是把通过不同传感器、不同时间拍摄、不同视觉的人脸图像统一到一个框架下,便于后续的特征提取和识别。人脸面部的特征提取是实现人脸识别技术的前提和基础,因此研究高效且鲁棒性更好的特征提取方法具有十分重要的现实意义。
本论文以特征提取方法和图像匹配技术为研究目标,以人脸识别为应用背景,针对不同的特征提取方法进行深入探讨和研究,所提出的一些改进算法能有效地提高人脸识别率。主要研究内容和成果包括：
(1)图像匹配的目的是把两幅或多幅图像在空间上进行对准,以确定它们之间存在某种变换关系的过程,这些图像是在不同时间、不同传感器和不同视角下拍摄得到的。针对图像匹配技术,本文阐述了它的基本定义、流程以及相关的匹配方法,在详细研究基础上提出了一种基于投影熵的图像匹配技术。
(2)针对直接采用奇异值分解(Singular Value Decomposition, SVD)中的奇异值向量进行人脸识别的识别率较低问题,本文采用基于类估计基空间的改进SVD计算人脸图像的奇异值特征向量,为了降低光照、表情、噪声、姿态等多因素的影响,提出了一种融合多尺度全局特征和局部特征的人脸特征表示方法。在得到多尺度融合人脸特征基础上采用粗糙集约简算法进行特征选择,最后把选择的特征作为SVM分类器的输入从而进行人脸识别。实验结果表明,多尺度下融合全局特征和局部特征的识别方法是一种更有效的特征提取方法。
(3)针对人脸识别中的光照问题,分析一些传统光照处理方法,深入研究和分析基于全变分模型(Total variation model, TV)的光照处理算法。本文在研究全变分模型的基础上,采用L1范数优化技术并结合Bregman迭代算法求解反射系数。为了提高模型的处理速度,采用基于多分辨率的处理方法从粗糙到精细的逐步迭代求解过程,最后结合分块PCA方法提取人脸面部的局部特征,使得局部特征更能充分表征和描述图像间的差异性和相似性。实验结果表明,该方法能更有效地提取人脸图像的局部特征,更准确地表征人脸面部的结构特征和纹理信息。
(4)针对经典主动形状模型(Active shape model, ASM)直接采用灰度值信息构建局部轮廓模型,灰度值对光照、噪声等因素是十分敏感的,本文采用每个像素点的边缘结构方向代替灰度值构建局部轮廓模型,提出了一种改进的局部轮廓模型建立方法。该方法在法线方向的两侧采样灰度点,在每个灰度点的邻域内再采样若干点,能充分有效地利用每个采样点以及它所在邻域内点的灰度分布信息。实验结果表明,改进ASM方法取得了较好的特征点定位结果,基于改进ASM特征提取的人脸识别率也有较大提高。
(5)针对经典ASM采用PCA变换获得形状主成分向量并结合形状参数建立一个线性统计形状模型,传统PCA方法不能实时有效地更新模型中的协方差矩阵和平均纹理轮廓。本文提出一种改进的ASM方法,该方法采用增量子空间学习方法更新图像训练集的特征空间,不断更新的特征空间能很好地描述图像之间特征结构信息。实验结果表明,改进的方法可以有效提高人脸特征点的定位精度,提高人脸识别的识别率。
本论文通过对人脸识别中特征提取方法和图像匹配技术的研究,提出了一些改进的特征提取和识别方法。深入分析基于奇异值的人脸识别方法,提出了一种融合多尺度全局特征和局部特征的人脸识别方法。针对人脸识别中的光照问题,构造一种基于全变分模型且鲁棒性更好的特征提取方法。在研究经典统计模型的基础上,提出了更加精准的基于ASM的特征定位方法,改进的两种ASM方法能有效提高定位精度。通过理论研究和实验仿真相结合的方法,为人脸识别技术中的特征提取方法及其相关领域的深入研究进行了有益的探索。
With the rapid development of information technology, artificial intelligence, pattern recognition, computer vision and other new technologies,face recognition has been widely used in public safety, information security, finance and other fields. It has become one of the hot topics of research in image processing fields in recent years. For a face recognition system, the purpose of image matching is to unify these face images from different sensors, different times, different visual scenes into a framework, in order to facilitate subsequent feature extraction and recognition. Therefore, feature extraction is the premise and basis of face recognition technology, it is very critical to study some fast and efficient methods for human facial feature extraction.
The main aim of this work is to propose feature extraction and image matching techniques. With face recognition as application background, different feature extraction methods will be explored deeply. The main contents and results are given as follows:
(1) The aim of image matching is to implement two or more images to be aligned in the same space, which is used to determine the existence transformation relationship among them, and these images are from different time, different sensor and different scene. The basic definition and process of image matching are described, and the image matching method based on projection entropy is given in detail.
(2) A new and robust face recognition method is proposed to overcome the flaw with low recognition rate to be used singular value vector-based recognition method. The class estimated basis space method is used to extract facial singular value, and then a method fuses multi-scale global and local features in order to reduce the effects of illumination, expression, noise, pose and other factors.The rough set reduction algorithm is used to select feature, and ultimately selected features are as the input into the SVM classifier. Experimental results show that the improved method has the validity, and the method fused multi-scale integration of the global and local feature is a more efficient feature extraction method.
(3) To illumination challenge in face recognition, the illumination processing method based on total variation model is given in detail. On the basis of total variation model, the L1optimization combined with Bregman Iterative Algorithm is used to solve the reflection coefficient. In order to improve the processing speed of the model, the multi-resolution method is applied to find optimal solution from a rough value to the best solution. Finally, modular PCA method is used to extract the local features to describe difference and similarity among face images more effectively. Experimental results show the effectiveness of the method, and the improved method has obtained better recognition rate.
(4) Intensity values used in original ASM can't provide enough information for model searching, which is also sensitive to lighting conditions and so on. So we use a measure which indicates the orientation of structure at each pixel instead of intensity value to represent image texture. In addition, a new method is adopted for building local profile model, which makes full use of texture information around the landmarks. Experimental results show that the improved ASM can locate face features more accurately than original ASM, and face recognition rate is much larger based on the improved ASM feature extraction method than that of original ASM.
(5) In original ASM model, the Principal Component Analysis (PCA) approach is used to extract shape eigenvectors of the training data. The traditional PCA method can't real-time to update the covariance matrix and mean texture, which is sensitive to pose, illumination and expression variations in images. An improved method is propose to overcome the flaw, the feature space is constantly updated by using the incremental principal component analysis (IPCA), which it can describe the similarity or difference among training image sets. The experimental results show that the improved method can effectively improve the matching accuracy and it can also improve the face recognition rate.
In this thesis, some improved feature extraction methods are proposed by deeply researching about feature extraction algorithm and image matching technology for face recognition. To overcome these flaws of singular value vector-based recognition method, a new and robust face recognition method based on fusion global and local feature is proposed. To illumination challenge in face recognition, a novel and robust feature extraction method based on total variation model is constructed. After analyzing the basis of statistical model, two improved ASM can locate facial feature more accurately. By the way of combining theory with experimental results, it is a useful exploration process for the deep study of relative fields of face recognition and feature extraction.
本论文以特征提取方法和图像匹配技术为研究目标,以人脸识别为应用背景,针对不同的特征提取方法进行深入探讨和研究,所提出的一些改进算法能有效地提高人脸识别率。主要研究内容和成果包括：
(1)图像匹配的目的是把两幅或多幅图像在空间上进行对准,以确定它们之间存在某种变换关系的过程,这些图像是在不同时间、不同传感器和不同视角下拍摄得到的。针对图像匹配技术,本文阐述了它的基本定义、流程以及相关的匹配方法,在详细研究基础上提出了一种基于投影熵的图像匹配技术。
(2)针对直接采用奇异值分解(Singular Value Decomposition, SVD)中的奇异值向量进行人脸识别的识别率较低问题,本文采用基于类估计基空间的改进SVD计算人脸图像的奇异值特征向量,为了降低光照、表情、噪声、姿态等多因素的影响,提出了一种融合多尺度全局特征和局部特征的人脸特征表示方法。在得到多尺度融合人脸特征基础上采用粗糙集约简算法进行特征选择,最后把选择的特征作为SVM分类器的输入从而进行人脸识别。实验结果表明,多尺度下融合全局特征和局部特征的识别方法是一种更有效的特征提取方法。
(3)针对人脸识别中的光照问题,分析一些传统光照处理方法,深入研究和分析基于全变分模型(Total variation model, TV)的光照处理算法。本文在研究全变分模型的基础上,采用L1范数优化技术并结合Bregman迭代算法求解反射系数。为了提高模型的处理速度,采用基于多分辨率的处理方法从粗糙到精细的逐步迭代求解过程,最后结合分块PCA方法提取人脸面部的局部特征,使得局部特征更能充分表征和描述图像间的差异性和相似性。实验结果表明,该方法能更有效地提取人脸图像的局部特征,更准确地表征人脸面部的结构特征和纹理信息。
(4)针对经典主动形状模型(Active shape model, ASM)直接采用灰度值信息构建局部轮廓模型,灰度值对光照、噪声等因素是十分敏感的,本文采用每个像素点的边缘结构方向代替灰度值构建局部轮廓模型,提出了一种改进的局部轮廓模型建立方法。该方法在法线方向的两侧采样灰度点,在每个灰度点的邻域内再采样若干点,能充分有效地利用每个采样点以及它所在邻域内点的灰度分布信息。实验结果表明,改进ASM方法取得了较好的特征点定位结果,基于改进ASM特征提取的人脸识别率也有较大提高。
(5)针对经典ASM采用PCA变换获得形状主成分向量并结合形状参数建立一个线性统计形状模型,传统PCA方法不能实时有效地更新模型中的协方差矩阵和平均纹理轮廓。本文提出一种改进的ASM方法,该方法采用增量子空间学习方法更新图像训练集的特征空间,不断更新的特征空间能很好地描述图像之间特征结构信息。实验结果表明,改进的方法可以有效提高人脸特征点的定位精度,提高人脸识别的识别率。
本论文通过对人脸识别中特征提取方法和图像匹配技术的研究,提出了一些改进的特征提取和识别方法。深入分析基于奇异值的人脸识别方法,提出了一种融合多尺度全局特征和局部特征的人脸识别方法。针对人脸识别中的光照问题,构造一种基于全变分模型且鲁棒性更好的特征提取方法。在研究经典统计模型的基础上,提出了更加精准的基于ASM的特征定位方法,改进的两种ASM方法能有效提高定位精度。通过理论研究和实验仿真相结合的方法,为人脸识别技术中的特征提取方法及其相关领域的深入研究进行了有益的探索。
With the rapid development of information technology, artificial intelligence, pattern recognition, computer vision and other new technologies,face recognition has been widely used in public safety, information security, finance and other fields. It has become one of the hot topics of research in image processing fields in recent years. For a face recognition system, the purpose of image matching is to unify these face images from different sensors, different times, different visual scenes into a framework, in order to facilitate subsequent feature extraction and recognition. Therefore, feature extraction is the premise and basis of face recognition technology, it is very critical to study some fast and efficient methods for human facial feature extraction.
The main aim of this work is to propose feature extraction and image matching techniques. With face recognition as application background, different feature extraction methods will be explored deeply. The main contents and results are given as follows:
(1) The aim of image matching is to implement two or more images to be aligned in the same space, which is used to determine the existence transformation relationship among them, and these images are from different time, different sensor and different scene. The basic definition and process of image matching are described, and the image matching method based on projection entropy is given in detail.
(2) A new and robust face recognition method is proposed to overcome the flaw with low recognition rate to be used singular value vector-based recognition method. The class estimated basis space method is used to extract facial singular value, and then a method fuses multi-scale global and local features in order to reduce the effects of illumination, expression, noise, pose and other factors.The rough set reduction algorithm is used to select feature, and ultimately selected features are as the input into the SVM classifier. Experimental results show that the improved method has the validity, and the method fused multi-scale integration of the global and local feature is a more efficient feature extraction method.
(3) To illumination challenge in face recognition, the illumination processing method based on total variation model is given in detail. On the basis of total variation model, the L1optimization combined with Bregman Iterative Algorithm is used to solve the reflection coefficient. In order to improve the processing speed of the model, the multi-resolution method is applied to find optimal solution from a rough value to the best solution. Finally, modular PCA method is used to extract the local features to describe difference and similarity among face images more effectively. Experimental results show the effectiveness of the method, and the improved method has obtained better recognition rate.
(4) Intensity values used in original ASM can't provide enough information for model searching, which is also sensitive to lighting conditions and so on. So we use a measure which indicates the orientation of structure at each pixel instead of intensity value to represent image texture. In addition, a new method is adopted for building local profile model, which makes full use of texture information around the landmarks. Experimental results show that the improved ASM can locate face features more accurately than original ASM, and face recognition rate is much larger based on the improved ASM feature extraction method than that of original ASM.
(5) In original ASM model, the Principal Component Analysis (PCA) approach is used to extract shape eigenvectors of the training data. The traditional PCA method can't real-time to update the covariance matrix and mean texture, which is sensitive to pose, illumination and expression variations in images. An improved method is propose to overcome the flaw, the feature space is constantly updated by using the incremental principal component analysis (IPCA), which it can describe the similarity or difference among training image sets. The experimental results show that the improved method can effectively improve the matching accuracy and it can also improve the face recognition rate.
In this thesis, some improved feature extraction methods are proposed by deeply researching about feature extraction algorithm and image matching technology for face recognition. To overcome these flaws of singular value vector-based recognition method, a new and robust face recognition method based on fusion global and local feature is proposed. To illumination challenge in face recognition, a novel and robust feature extraction method based on total variation model is constructed. After analyzing the basis of statistical model, two improved ASM can locate facial feature more accurately. By the way of combining theory with experimental results, it is a useful exploration process for the deep study of relative fields of face recognition and feature extraction.
引文
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