自动人脸分析与识别的若干问题研究
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摘要
自动人脸分析与识别通过计算机自动地完成包括人脸检测/跟踪、人脸特征定位、人脸识别、表情识别以及人脸重建(2D/3D)等问题,是计算机视觉、模式识别以及人工智能等领域内一个极具重要意义和应用价值的研究课题,在科学研究(推进人工智能发展)、安防领域(基于人脸分析的关键场所控制等)、娱乐动画(虚拟人脸合成、三维人脸建模、非真实感人脸合成等)以及科学教育等方面都有着广泛的应用价值。本文主要针对自动人脸分析与识别中的若干关键问题进行了深入的讨论与分析,重点进行了如下几个方面的研究：人脸特征点检测与定位、人脸非真实感绘制研究、融合时频域分块的人脸识别以及草图人脸识别技术等。本文的主要工作内容和创新点如下：
1.以IBM规划、网易邮箱以及奥运安防三个实例引出人脸识别技术的重要性,不仅在科学研究上,而且在实际应用中。介绍了人脸分析与识别中的三个主要部分：人脸识别、表情识别以及人脸合成。本文主要针对人脸识别技术进行了较为详细的论述,阐述了人脸识别的意义与应用,并对目前人脸识别各方向及其主流算法进行了较为详尽的讨论,此外,还对人脸识别技术研究中常用的一些主流人脸数据库进行了汇总。
2.研究了人脸特征点检测与定位问题。阐述了人脸特征点定位是人脸分析中的一项重要研究内容以及在本文余下一些章节的研究中(人脸线条画绘制、草图人脸识别等)具有重要的作用。针对传统的ASM算法由于局部纹理模型过于简单易陷入寻优过程中的局部最小的问题,本文提出了一些改进算法：首先针对以往利用灰度信息获取瞳孔位置易受光照等影响的特点,利用最小二乘法定位瞳孔位置,通过瞳孔位置来初始化全局形状模型；其次针对人脸肤色在YCrCb空间中具有较好的聚类效果,建立基于YCrCb空间的加权局部纹理模型,新的局部纹理模型融合更多实际特征点周围区域的信息,因而对特征点的检测更加有力。实验结果表明基于最小二乘法的瞳孔定位可以更精确的确定瞳孔位置,新的局部纹理模型对于减少ASM算法在特征点定位上的错误率,提高定位精度,都有一定的改进。
3.研究和讨论了人脸非真实感绘制技术。人脸线条画是一种重要的非真实感人脸艺术形式,被广泛用于网络娱乐,科教文卫,非真实感绘画研究等领域。本文提出了一种新的人脸线条画绘制方法,该方法融合了样本学习以及局部图像处理技术,使得能够准确高效的绘制人脸线条画。算法首先利用基于Canny人脸轮廓提取得到人脸的粗略轮廓和大致形貌,使用ASM算法进行人脸特征定位并在人脸特征分割的基础上针对不同的特征选择合适的图像处理算法以及曲线拟合技术得到人脸的局部特征线条化处理,也就是人脸器官的精确轮廓,最后将粗轮廓与精轮廓进行组合得到人脸的线条画；为了实现线条画的绘制,本文提出了一种递归邻接优先矢量化算法以实现线条画的矢量化。我们使用VC++开发并实现了该人脸线条画绘制系统,系统运行稳定,处理结果令人满意。另外还讨论了基于像素的人脸非真实感绘制。
4.针对特征融合和图像分块在人脸识别中的重要作用,本文提出了一种基于时频域分块融合全局和局部特征的人脸识别算法。首先,通过分析二维小波包变换与图像分组策略,我们发现基于频域分析的小波包变换能够更好的满足图像分组策略,同时还提出了局部矩阵主成分分析(LMPCA), LMPCA通过抽取频域特征建立全局分类器能够很好的反映人脸模式结构特征以及不同小波包系数矩阵之间的关联性信息；其次,在分析之前图像分块模式的基础上,提出了人脸环形分块策略,能够更好的反应人脸的拓扑信息与器官分布特征；最后将全局和局部分类器通过特定的加权方式融合成联合分类器。ORL和FERET人脸数据库被用来验证上述方法的有效性,并将其与其他主流人脸识别方法进行比较,我们提出的算法抽取的特征具有更好的鉴别力且识别率也较高。
5.草图人脸识别是近几年才出现的一个人脸识别新领域,本文对草图人脸识别做了比较详细的论述和方法综述。本文给出了两种草图人脸识别算法,首先研究了基于ICA独立特征子空间下的草图人脸合成与识别,ICA算法充分利用了图像的高阶统计信息,且比PCA在空间上更加局部化,实验也表明基于ICA的伪草图合成要优于基于PCA的；另外,人对于草图人脸的识别是综合了人脸纹理特征和结构信息的,本文提出极坐标形状模型(PSM),利用该模型在草图人脸识别中引入人脸结构信息,提取草图和照片在结构上的相似性,基于ICA的伪草图合成以及基于PSM的人脸结构信息的联合能够实现草图人脸识别。其次,由于伪草图合成是一个比草图识别更加困难的问题,而一般人脸草图数据库对于每个人只有一张照片和一张草图,因此草图人脸识别本质上是一种单样本人脸识别,本文中还提出了一种基于中心误差扩散局部二值模式(CEDLBP)(?)草图人脸识别方法,通过编码人脸照片和草图,将其投影在一个中间空间中以降低模式差异,对图像进行小波包分解并利用CEDLBP编码来扩充样本,最后使用PCA+LDA来识别草图,实验表明提出的方法具有较好的表现和较高的识别率。
The research of auto face analysis and recognition has important significance and application value in the field of computer vision, pattern recognition, and artificial intelligence, which can deal with the problem of face detection, face feature location, face recognition, expression recognition, and face reconstruction(2D/3D) by using computer analysis. It is widely used in science research (artificial intelligence), security (staying security in public based on face analysis), entertainment and animation (virtual face synthesis,3D face modeling, non-photorealistic face synthesis, etc), and education. This thesis deeply study some key problems of face analysis and recognition, including face feature point detection, face non-photorealistic drawing, face recognition fusing spatial and frequency blocking and face sketch recognition. This thesis obtains the following contents and innovations:
1. The face recognition is not only important to research, but also practical to apply. We expound these by given three examples, such as IBM planning, netease mailbox, and the Olympic security. In the first section, we introduce the three major parts of face analysis and recognition, which are face recognition, expression recognition, and face synthesis. This thesis mainly describes the face recognition technology in detail and expounds its significance and application, and makes a more detailed discussion on the direction and mainstream of face recognition algorithm. In addition, it also summarizes the main face image databases that face recognition technology commonly used.
2. The face feature point detection and localization problem is studied in this thesis. Above all, the important role of face feature point positioning in the face analysis is illustrated. It also has the vital role in some studies (face line drawings, face sketch recognition) in the rest of chapters. Aiming at the problem of the local texture model of traditional ASM is simple which is easy to make ASM into local minimum in the process of optimization, this thesis proposed some modified methods: firstly, using gray information to acquire pupil position is affected easily by illumination, least square method was used to locate pupil position, the pupil position can initialize global shape model and make most feature points close to its good position; secondly, building weighted local texture model based on YCrCb space, in view of the face skin color in YCrCb space has good clustering effect, and the areas in the positive and negative normal directions of face feature point are commonly skin or non-skin color, and we established texture models in skin color area based on skin clustering and in the non-skin color area, and established traditional texture model in the feature point area, respectively. The new local texture model fused more information in the real feature point, which also contain the information of the around area, thus it is more powerful to detect feature point. The experimental results show that detecting pupil based on least square method can be more precise. Meantime, the new local texture model can reduce the positioning error rate of ASM algorithm and improve the location precision.
3. We also study the face non-photorealistic painting technology. Face line drawings is an important non-photorealistic face art form, which is widely used in entertainment, science-education and the non-photorealistic painting research, etc. This thesis proposes a new face line paints drawing method. This method combines sample learning method and local image processing method, making accurate and efficient face line drawings, and realize its vectorization. The algorithm used Canny operator to extract face contour and obtained a rough outline which including the outline of the face and hair, then used ASM to locate face features (got the precise localization of human eye, nose, mouth) and chose the right image processing algorithm or curve fitting technique to get the line processing of local characteristics, getting the precise outline of face organs. At last, we combined the rough outline and fine outline to get face line drawings. In order to drawing the line paints, this thesis proposed recursion adjacency priority vectorization algorithm. We used the VC++to develop and realize the face line drawings painting system, the system runs stably and results are satisfied. In addition, we also discussed non-photorealistic face painting based on pixel.
4. According to characteristics fusion and image blocking have important role in face recognition, this thesis provided a fusing global and local features based on spatial and frequency blocking face recognition algorithm. Firstly, by analyzing two-dimensional wavelet packet transform and image group strategy, we found that wavelet packet transform based on frequency domain analysis can better satisfy the image group strategy, at the same time, the thesis also proposed the local matrix principal component analysis (LMPCA), LMPCA can build the global classifier by extracting frequency domain features and reflect face model structural features and the correlation information between different wavelet packet coefficient matrix. Secondly, by analyzing the advantages and disadvantages of traditional image blocking methods, this thesis proposed the annular blocking strategy and it can better reflect face topology information and the organ distribution characteristics. KLDA is used to extract the nonlinear local characteristics and build local classifiers. At last, global and local classifiers merge into joint classifier by weighted way. ORL and FERET face image databases are used to verify the effectiveness of the method, and the other mainstream face recognition methods are compared with it, our method has a higher recognition rate.
5. Face sketch recognition is a new field in recent years, this thesis made a detailed description and survey of face sketch recognition and its methods. We researched two kinds of face sketch recognition algorithms, Firstly, we studied face sketch synthesis and recognition based on ICA. ICA makes full use of the image higher order statistics information and can be more localization than PCA algorithm. The experiment also showed that the synthesized sketch based on ICA is superior to PCA; in addition, in order to recognize sketch, people also consider the face structural information, this thesis proposed polar shape model (PSM) which can introduce face structure information to face sketch recognition by extracting the structural similarity between sketch and photo. Using the photos-sketch conversion relationship to convert photo to pseudo-sketch, then combined the sketch and pseudo-sketch extraction features based on ICA/LDA and shape projection vector based on PSM for sketch recognition. Secondly, since sketches synthesis is a more difficult problem than sketch recognition, and generally face sketch database have only a photo and a sketch for each person, thus face sketch recognition is essentially a single sample face recognition, in this study we proposed a new face sketch recognition method. This method coded face photo and sketch through the center error diffusion local binary pattern (CEDLBP), photo and sketch are projected into a middle space, for the single sample face recognition problem, we did image wavelet packet decomposition and used CEDLBP to code expand samples, and finally used PCA+LDA to identify sketch, experiments showed that the proposed method has better performance and higher recognition rate.
1.以IBM规划、网易邮箱以及奥运安防三个实例引出人脸识别技术的重要性,不仅在科学研究上,而且在实际应用中。介绍了人脸分析与识别中的三个主要部分：人脸识别、表情识别以及人脸合成。本文主要针对人脸识别技术进行了较为详细的论述,阐述了人脸识别的意义与应用,并对目前人脸识别各方向及其主流算法进行了较为详尽的讨论,此外,还对人脸识别技术研究中常用的一些主流人脸数据库进行了汇总。
2.研究了人脸特征点检测与定位问题。阐述了人脸特征点定位是人脸分析中的一项重要研究内容以及在本文余下一些章节的研究中(人脸线条画绘制、草图人脸识别等)具有重要的作用。针对传统的ASM算法由于局部纹理模型过于简单易陷入寻优过程中的局部最小的问题,本文提出了一些改进算法：首先针对以往利用灰度信息获取瞳孔位置易受光照等影响的特点,利用最小二乘法定位瞳孔位置,通过瞳孔位置来初始化全局形状模型；其次针对人脸肤色在YCrCb空间中具有较好的聚类效果,建立基于YCrCb空间的加权局部纹理模型,新的局部纹理模型融合更多实际特征点周围区域的信息,因而对特征点的检测更加有力。实验结果表明基于最小二乘法的瞳孔定位可以更精确的确定瞳孔位置,新的局部纹理模型对于减少ASM算法在特征点定位上的错误率,提高定位精度,都有一定的改进。
3.研究和讨论了人脸非真实感绘制技术。人脸线条画是一种重要的非真实感人脸艺术形式,被广泛用于网络娱乐,科教文卫,非真实感绘画研究等领域。本文提出了一种新的人脸线条画绘制方法,该方法融合了样本学习以及局部图像处理技术,使得能够准确高效的绘制人脸线条画。算法首先利用基于Canny人脸轮廓提取得到人脸的粗略轮廓和大致形貌,使用ASM算法进行人脸特征定位并在人脸特征分割的基础上针对不同的特征选择合适的图像处理算法以及曲线拟合技术得到人脸的局部特征线条化处理,也就是人脸器官的精确轮廓,最后将粗轮廓与精轮廓进行组合得到人脸的线条画；为了实现线条画的绘制,本文提出了一种递归邻接优先矢量化算法以实现线条画的矢量化。我们使用VC++开发并实现了该人脸线条画绘制系统,系统运行稳定,处理结果令人满意。另外还讨论了基于像素的人脸非真实感绘制。
4.针对特征融合和图像分块在人脸识别中的重要作用,本文提出了一种基于时频域分块融合全局和局部特征的人脸识别算法。首先,通过分析二维小波包变换与图像分组策略,我们发现基于频域分析的小波包变换能够更好的满足图像分组策略,同时还提出了局部矩阵主成分分析(LMPCA), LMPCA通过抽取频域特征建立全局分类器能够很好的反映人脸模式结构特征以及不同小波包系数矩阵之间的关联性信息；其次,在分析之前图像分块模式的基础上,提出了人脸环形分块策略,能够更好的反应人脸的拓扑信息与器官分布特征；最后将全局和局部分类器通过特定的加权方式融合成联合分类器。ORL和FERET人脸数据库被用来验证上述方法的有效性,并将其与其他主流人脸识别方法进行比较,我们提出的算法抽取的特征具有更好的鉴别力且识别率也较高。
5.草图人脸识别是近几年才出现的一个人脸识别新领域,本文对草图人脸识别做了比较详细的论述和方法综述。本文给出了两种草图人脸识别算法,首先研究了基于ICA独立特征子空间下的草图人脸合成与识别,ICA算法充分利用了图像的高阶统计信息,且比PCA在空间上更加局部化,实验也表明基于ICA的伪草图合成要优于基于PCA的；另外,人对于草图人脸的识别是综合了人脸纹理特征和结构信息的,本文提出极坐标形状模型(PSM),利用该模型在草图人脸识别中引入人脸结构信息,提取草图和照片在结构上的相似性,基于ICA的伪草图合成以及基于PSM的人脸结构信息的联合能够实现草图人脸识别。其次,由于伪草图合成是一个比草图识别更加困难的问题,而一般人脸草图数据库对于每个人只有一张照片和一张草图,因此草图人脸识别本质上是一种单样本人脸识别,本文中还提出了一种基于中心误差扩散局部二值模式(CEDLBP)(?)草图人脸识别方法,通过编码人脸照片和草图,将其投影在一个中间空间中以降低模式差异,对图像进行小波包分解并利用CEDLBP编码来扩充样本,最后使用PCA+LDA来识别草图,实验表明提出的方法具有较好的表现和较高的识别率。
The research of auto face analysis and recognition has important significance and application value in the field of computer vision, pattern recognition, and artificial intelligence, which can deal with the problem of face detection, face feature location, face recognition, expression recognition, and face reconstruction(2D/3D) by using computer analysis. It is widely used in science research (artificial intelligence), security (staying security in public based on face analysis), entertainment and animation (virtual face synthesis,3D face modeling, non-photorealistic face synthesis, etc), and education. This thesis deeply study some key problems of face analysis and recognition, including face feature point detection, face non-photorealistic drawing, face recognition fusing spatial and frequency blocking and face sketch recognition. This thesis obtains the following contents and innovations:
1. The face recognition is not only important to research, but also practical to apply. We expound these by given three examples, such as IBM planning, netease mailbox, and the Olympic security. In the first section, we introduce the three major parts of face analysis and recognition, which are face recognition, expression recognition, and face synthesis. This thesis mainly describes the face recognition technology in detail and expounds its significance and application, and makes a more detailed discussion on the direction and mainstream of face recognition algorithm. In addition, it also summarizes the main face image databases that face recognition technology commonly used.
2. The face feature point detection and localization problem is studied in this thesis. Above all, the important role of face feature point positioning in the face analysis is illustrated. It also has the vital role in some studies (face line drawings, face sketch recognition) in the rest of chapters. Aiming at the problem of the local texture model of traditional ASM is simple which is easy to make ASM into local minimum in the process of optimization, this thesis proposed some modified methods: firstly, using gray information to acquire pupil position is affected easily by illumination, least square method was used to locate pupil position, the pupil position can initialize global shape model and make most feature points close to its good position; secondly, building weighted local texture model based on YCrCb space, in view of the face skin color in YCrCb space has good clustering effect, and the areas in the positive and negative normal directions of face feature point are commonly skin or non-skin color, and we established texture models in skin color area based on skin clustering and in the non-skin color area, and established traditional texture model in the feature point area, respectively. The new local texture model fused more information in the real feature point, which also contain the information of the around area, thus it is more powerful to detect feature point. The experimental results show that detecting pupil based on least square method can be more precise. Meantime, the new local texture model can reduce the positioning error rate of ASM algorithm and improve the location precision.
3. We also study the face non-photorealistic painting technology. Face line drawings is an important non-photorealistic face art form, which is widely used in entertainment, science-education and the non-photorealistic painting research, etc. This thesis proposes a new face line paints drawing method. This method combines sample learning method and local image processing method, making accurate and efficient face line drawings, and realize its vectorization. The algorithm used Canny operator to extract face contour and obtained a rough outline which including the outline of the face and hair, then used ASM to locate face features (got the precise localization of human eye, nose, mouth) and chose the right image processing algorithm or curve fitting technique to get the line processing of local characteristics, getting the precise outline of face organs. At last, we combined the rough outline and fine outline to get face line drawings. In order to drawing the line paints, this thesis proposed recursion adjacency priority vectorization algorithm. We used the VC++to develop and realize the face line drawings painting system, the system runs stably and results are satisfied. In addition, we also discussed non-photorealistic face painting based on pixel.
4. According to characteristics fusion and image blocking have important role in face recognition, this thesis provided a fusing global and local features based on spatial and frequency blocking face recognition algorithm. Firstly, by analyzing two-dimensional wavelet packet transform and image group strategy, we found that wavelet packet transform based on frequency domain analysis can better satisfy the image group strategy, at the same time, the thesis also proposed the local matrix principal component analysis (LMPCA), LMPCA can build the global classifier by extracting frequency domain features and reflect face model structural features and the correlation information between different wavelet packet coefficient matrix. Secondly, by analyzing the advantages and disadvantages of traditional image blocking methods, this thesis proposed the annular blocking strategy and it can better reflect face topology information and the organ distribution characteristics. KLDA is used to extract the nonlinear local characteristics and build local classifiers. At last, global and local classifiers merge into joint classifier by weighted way. ORL and FERET face image databases are used to verify the effectiveness of the method, and the other mainstream face recognition methods are compared with it, our method has a higher recognition rate.
5. Face sketch recognition is a new field in recent years, this thesis made a detailed description and survey of face sketch recognition and its methods. We researched two kinds of face sketch recognition algorithms, Firstly, we studied face sketch synthesis and recognition based on ICA. ICA makes full use of the image higher order statistics information and can be more localization than PCA algorithm. The experiment also showed that the synthesized sketch based on ICA is superior to PCA; in addition, in order to recognize sketch, people also consider the face structural information, this thesis proposed polar shape model (PSM) which can introduce face structure information to face sketch recognition by extracting the structural similarity between sketch and photo. Using the photos-sketch conversion relationship to convert photo to pseudo-sketch, then combined the sketch and pseudo-sketch extraction features based on ICA/LDA and shape projection vector based on PSM for sketch recognition. Secondly, since sketches synthesis is a more difficult problem than sketch recognition, and generally face sketch database have only a photo and a sketch for each person, thus face sketch recognition is essentially a single sample face recognition, in this study we proposed a new face sketch recognition method. This method coded face photo and sketch through the center error diffusion local binary pattern (CEDLBP), photo and sketch are projected into a middle space, for the single sample face recognition problem, we did image wavelet packet decomposition and used CEDLBP to code expand samples, and finally used PCA+LDA to identify sketch, experiments showed that the proposed method has better performance and higher recognition rate.
引文
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