Human face recognition based on spatially weighted Hausdorff distance

The edge map of a facial image contains abundant information about its shape and structure, which is useful for face recognition. To compare edge images, Hausdorff distance is an efficient measure that can determine the degree of their resemblance, and does not require a knowledge of correspondence among those points in the two edge maps. In this paper, a new modified Hausdorff distance measure is proposed, which has a better discriminant power. As different facial regions have different degrees of significance for face recognition, a new modified Hausdorff distance is proposed which is weighted according to a weighted function derived from the spatial information of the human face; hence crucial regions are emphasized for face identification. Experimental results show that the distance measure can achieve recognition rates of 80%, 87%, and 91% for the first, the first five, and the first seven likely matched faces, respectively.     

COMPARING FACE IMAGES USING THE MODIFIED HAUSDORFF DISTANCE

We introduce a novel methodology applicable to face matching and fast screening of large facial databases. The proposed shape comparison method operates on edge maps and derives holistic similarity measures, yet, it does not require solving the point correspondence problem. While the use of edge images is important to introduce robustness to changes in illumination, the lack of point-to-point matching delivers speed and tolerance to local non-rigid distortions. In particular, we propose a face similarity measure derived as a variant of the Hausdorff distance by introducing the notion of a neighborhood function (N) and associated penalties (P). Experimental results on a large set of face images demonstrate that our approach produces excellent recognition results even when less than 3% of the original grey-scale face image information is stored in the face database (gallery). These results implicate that the process of face recognition may start at a much earlier stage of visual processing than it was earlier suggested. We argue, that edge-like retinal images of faces are initially screened “at a glance” without the involvement of high-level cognitive functions thus delivering high speed and reducing computational complexity.     

基于相似度加权的自适应HD算法

Hausdorff距离(Hausdorff distance, HD)是一种点集与点集之间的距离测度, 常用于目标物体的匹配、跟踪和识别等. 本文在分析经典HD及改进算法的基础上, 提出了一种基于相似度加权的自适应HD (Adaptive Hausdarff distance, AHD)算法. AHD算法利用不同点到点集的最小距离的个数作为匹配相似度的测量, 并舍弃对判断匹配几乎没有作用的较大的点到点集的最小距离值; 同时根据点到点集的最小距离自适应选择权值, 从而得到一种基于相似度测量加权系数; 通过利用部分点到点集的最小距离和基于相似度的加权平均, 既增强了算法的鲁棒性, 又尽可能地保证了算法的精度. 实验结果显示, AHD算法在匹配准确性、抵抗噪声和遮挡干扰等方面性能良好.     

3D Face Similarity Measure by Fréchet Distances of Geodesics

3D face similarity is a critical issue in computer vision, computer graphics and face recognition and so on. Since Fréchet distance is an effective metric for measuring curve similarity, a novel 3D face similarity measure method based on Fréchet distances of geodesics is proposed in this paper. In our method, the surface similarity between two 3D faces is measured by the similarity between two sets of 3D curves on them. Due to the intrinsic property of geodesics, we select geodesics as the comparison curves. Firstly, the geodesics on each 3D facial model emanating from the nose tip point are extracted in the same initial direction with equal angular increment. Secondly, the Fréchet distances between the two sets of geodesics on the two compared facial models are computed. At last, the similarity between the two facial models is computed based on the Fréchet distances of the geodesics obtained in the second step. We verify our method both theoretically and practically. In theory, we prove that the similarity of our method satisfies three properties: reflexivity, symmetry, and triangle inequality. And in practice, experiments are conducted on the open 3D face database GavaDB, Texas 3D Face Recognition database, and our 3D face database. After the comparison with iso-geodesic and Hausdorff distance method, the results illustrate that our method has good discrimination ability and can not only identify the facial models of the same person, but also distinguish the facial models of any two different persons.     

Natural facial expression recognition using differential-AAM and manifold learning

This paper proposes a novel natural facial expression recognition method that recognizes a sequence of dynamic facial expression images using the differential active appearance model (AAM) and manifold learning as follows. First, the differential-AAM features (DAFs) are computed by the difference of the AAM parameters between an input face image and a reference (neutral expression) face image. Second, manifold learning embeds the DAFs on the smooth and continuous feature space. Third, the input facial expression is recognized through two steps: (1) computing the distances between the input image sequence and gallery image sequences using directed Hausdorff distance (DHD) and (2) selecting the expression by a majority voting of k-nearest neighbors (k-NN) sequences in the gallery. The DAFs are robust and efficient for the facial expression analysis due to the elimination of the inter-person, camera, and illumination variations. Since the DAFs treat the neutral expression image as the reference image, the neutral expression image must be found effectively. This is done via the differential facial expression probability density model (DFEPDM) using the kernel density approximation of the positively directional DAFs changing from neutral to angry (happy, surprised) and negatively directional DAFs changing from angry (happy, surprised) to neutral. Then, a face image is considered to be the neutral expression if it has the maximum DFEPDM in the input sequences. Experimental results show that (1) the DAFs improve the facial expression recognition performance over conventional AAM features by 20% and (2) the sequence-based k-NN classifier provides a 95% facial expression recognition performance on the facial expression database (FED06).     

Semi-intrusive multivariable model invalidation

This paper introduces a mechanism for testing multivariable models employed by model-based controllers. Although external excitation is not necessary, the data collection includes a stage where the controller is switched to open-loop operation (manual mode). The main idea is to measure a certain “distance” between the closed-loop and the open-loop signals, and then trigger a flag if this “distance” is larger than a threshold level. Moreover, a provision is made for accommodating model uncertainty. Since no hard bounds are assumed with respect to the noise amplitude, the model invalidation mechanism works in a probabilistic framework.     

Identification of actors drawn in Ukiyoe pictures

This paper presents the development of line image keywords for the identification of actors drawn in Japanese traditional painting pictures known as Ukiyoe pictures. The system is based on visual features of the face from the image database files and is organized as a set of classifiers whose outputs are integrated after a normalization step. Line profile from the picture has been extracted in this investigation and has been approximated by Bézier curves. A learning algorithm has been developed to obtain the control points at high accuracy. A new curve matching method has been developed based on the feature points, rather than the corresponding points. This method can automatically fit a set of data points with piecewise geometrically continuous third order Bézier curves. Last of all, a new approach for distance calculation, namely “apple-node distance” has been introduced here for similarity calculation in image retrieval systems. The computation of similarity between curves has been established on the basis of this “apple-node” distance. The effectiveness of our method has been confirmed through computer simulation. The method developed here can be expanded to one of three dimensional shape-analyzing tools.     

Anthropometric 3D Face Recognition

We present a novel anthropometric three dimensional (Anthroface 3D) face recognition algorithm, which is based on a systematically selected set of discriminatory structural characteristics of the human face derived from the existing scientific literature on facial anthropometry. We propose a novel technique for automatically detecting 10 anthropometric facial fiducial points that are associated with these discriminatory anthropometric features. We isolate and employ unique textural and/or structural characteristics of these fiducial points, along with the established anthropometric facial proportions of the human face for detecting them. Lastly, we develop a completely automatic face recognition algorithm that employs facial 3D Euclidean and geodesic distances between these 10 automatically located anthropometric facial fiducial points and a linear discriminant classifier. On a database of 1149 facial images of 118 subjects, we show that the standard deviation of the Euclidean distance of each automatically detected fiducial point from its manually identified position is less than 2.54 mm. We further show that the proposed Anthroface 3D recognition algorithm performs well (equal error rate of 1.98% and a rank 1 recognition rate of 96.8%), out performs three of the existing benchmark 3D face recognition algorithms, and is robust to the observed fiducial point localization errors.     

标准正面人脸图像的识别

本论文选用人脸上27个特殊点作为人脸基本特征。以人脸几何结构为基础,结合有脸识别的心理特性,提出新颖、简便、高精度的“寻找存在”法,使提取特征点的速度、精度得到大大的提高,在详细分析这27个特列点的统计特性后,选择了其中信息量丰富的15个点间距及间距比构成一组向量代替人脸描述,用加权欧氏距离作为特征向量间相似性测试,在两类实验中,识别率高达100％和98％。     

对表情鲁棒的面部轮廓线3维人脸识别

目的表情变化是3维人脸识别面临的主要问题。为克服表情影响,提出了一种基于面部轮廓线对表情鲁棒的3维人脸识别方法。方法首先,对人脸进行预处理,包括人脸区域切割、平滑处理和姿态归一化,将所有的人脸置于姿态坐标系下;然后,从3维人脸模型的半刚性区域提取人脸多条垂直方向的轮廓线来表征人脸面部曲面;最后,利用弹性曲线匹配算法计算不同3维人脸模型间对应的轮廓线在预形状空间(preshape space)中的测地距离,将其作为相似性度量,并且对所有轮廓线的相似度向量加权融合,得到总相似度用于分类。结果在FRGC v2.0数据库上进行识别实验,获得97.1%的Rank-1识别率。结论基于面部轮廓线的3维人脸识别方法,通过从人脸的半刚性区域提取多条面部轮廓线来表征人脸,在一定程度上削弱了表情的影响,同时还提高了人脸匹配速度。实验结果表明,该方法具有较强的识别性能,并且对表情变化具有较好的鲁棒性。     

Elastic shape-texture matching for human face recognition

In this paper, a novel, elastic, shape-texture matching method, namely ESTM, for human face recognition is proposed. In our approach, both the shape and the texture information are used to compare two faces without establishing any precise pixel-wise correspondence. The edge map is used to represent the shape of an image, while the texture information is characterized by both the Gabor representations and the gradient direction of each pixel. Combining these features, a shape-texture Hausdorff distance is devised to compute the similarity of two face images. The elastic matching is robust to small, local distortions of the feature points such as those caused by facial expression variations. In addition, the use of the edge map, Gabor representations and the direction of the image gradient can all alleviate the effect of illumination to a certain extent.With different databases, experimental results show that our algorithm can always achieve a better performance than other face recognition algorithms under different conditions, except when an image is under poor and uneven illumination. Experiments based on the Yale database, AR database, ORL database and YaleB database show that our proposed method can achieve recognition rates of 88.7%, 97.7%, 78.3% and 89.5%, respectively.     

Novel Multiclass Classifiers Based on the Minimization of the Within-Class Variance

In this paper, a novel class of multiclass classifiers inspired by the optimization of Fisher discriminant ratio and the support vector machine (SVM) formulation is introduced. The optimization problem of the so-called minimum within-class variance multiclass classifiers (MWCVMC) is formulated and solved in arbitrary Hilbert spaces, defined by Mercer's kernels, in order to find multiclass decision hyperplanes/surfaces. Afterwards, MWCVMCs are solved using indefinite kernels and dissimilarity measures via pseudo-Euclidean embedding. The power of the proposed approach is first demonstrated in the facial expression recognition of the seven basic facial expressions (i.e., anger, disgust, fear, happiness, sadness, and surprise plus the neutral state) problem in the presence of partial facial occlusion by using a pseudo-Euclidean embedding of Hausdorff distances and the MWCVMC. The experiments indicated a recognition accuracy rate achieved up to 99%. The MWCVMC classifiers are also applied to face recognition and other classification problems using Mercer's kernels.     

Face recognition based on 3D ridge images obtained from range data

In this paper, we present an approach for 3D face recognition from frontal range data based on the ridge lines on the surface of the face. We use the principal curvature, k_max, to represent the face image as a 3D binary image called ridge image. The ridge image shows the locations of the ridge points around the important facial regions on the face (i.e., the eyes, the nose, and the mouth). We utilized the robust Hausdorff distance and the iterative closest points (ICP) for matching the ridge image of a given probe image to the ridge images of the facial images in the gallery. To evaluate the performance of our approach for 3D face recognition, we performed experiments on GavabDB face database (a small size database) and Face Recognition Grand Challenge V2.0 (a large size database). The results of the experiments show that the ridge lines have great capability for 3D face recognition. In addition, we found that as long as the size of the database is small, the performance of the ICP-based matching and the robust Hausdorff matching are comparable. But, when the size of the database increases, ICP-based matching outperforms the robust Hausdorff matching technique.     

完善频谱脸人像识别的分类器设计

频谱脸方法是一种利用小波变换和Fourier变换有效地提取人像的位移不变特征和表情相对不变特征的方法。该文着重讨论了频谱脸方法系统化的预处理方法和相似性度量选择这两个关键性问题。其中，矩的方法被用于人像进行预处理，因为它能有效地对人像的伸缩和平面旋转进行矫正；通过对最近邻法、平均法、Hausdroff距离法和修正的Hausdroff距离法等4种典型的相似性度量方法中进行比较和分析的结果表明，最近邻法、平均法和修正的Hausdroff距离法都是频谱脸方法进行相似性度量的有效方法，其中，最近邻法是最有效的方法，它对诸如位移、伸缩、平面旋转、少许遮掩及少许姿势、表情和光照条件的变化多种影响人像识别的因素均具有最佳的容错性，并在Yale和Olivetti人 像数值库上进行了识别试验，分别取得了97％和99％的识别率。     

Example image-based feature extraction for face recognition

This paper proposes a novel method for recognizing facial images based on the relative distances between an input image and example images. Example facial images can be easily collected online, and a large example database can span new possible facial variations not sufficiently learned during the learning phase. We first extract facial features using a baseline classifier that has a certain degree of accuracy. To achieve a better performance of the proposed method, we divide the collected examples into groups using a clustering method (e.g., k-means), where each clustered group contains examples with similar characteristics. We then hierarchically partition a group formed in the previous level into other groups to analyze more specific facial characteristics, which represent an example pyramid. To describe the characteristics of a group using the clustered examples, we divide the example group into a number of sub-groups. We calculate the averages of the sub-groups and select an example most similar to the average in each sub-group because we assume that the averages of the sub-groups can directly represent their characteristics. Using the selected examples, we build example code words for a novel feature extraction. The example code words are used to measure the distances to an input image and serve as anchors to analyze a facial image in the example domain. The distance values are normalized for each group at all pyramid levels, and are concatenated to form novel features for face recognition. We verified the effectiveness of the proposed example pyramid framework using well-known proposed features, including LBP, HOG, Gabor, and the deep learning method, on the LFW database, and showed that it can yield significant improvements in recognition performance.     

基于特征点表情变化的3维人脸识别

目的 为克服表情变化对3维人脸识别的影响,提出一种基于特征点提取局部区域特征的3维人脸识别方法。方法 首先,在深度图上应用2维图像的ASM(active shape model)算法粗略定位出人脸特征点,再根据Shape index特征在人脸点云上精确定位出特征点。其次,提取以鼻中为中心的一系列等测地轮廓线来表征人脸形状;然后,提取具有姿态不变性的Procrustean向量特征(距离和角度)作为识别特征;最后,对各条等测地轮廓线特征的分类结果进行了比较,并对分类结果进行决策级融合。结果 在FRGC V2.0人脸数据库分别进行特征点定位实验和识别实验,平均定位误差小于2.36 mm,Rank-1识别率为98.35%。结论 基于特征点的3维人脸识别方法,通过特征点在人脸近似刚性区域提取特征,有效避免了受表情影响较大的嘴部区域。实验证明该方法具有较高的识别精度,同时对姿态、表情变化具有一定的鲁棒性。     

Face recognition using line edge map

The automatic recognition of human faces presents a significant challenge to the pattern recognition research community. Typically, human faces are very similar in structure with minor differences from person to person. They are actually within one class of "human face". Furthermore, lighting conditions change, while facial expressions and pose variations further complicate the face recognition task as one of the difficult problems in pattern analysis. This paper proposes a novel concept: namely, that faces can be recognized using a line edge map (LEM). The LEM, a compact face feature, is generated for face coding and recognition. A thorough investigation of the proposed concept is conducted which covers all aspects of human face recognition, i.e. face recognition under (1) controlled/ideal conditions and size variations, (2) varying lighting conditions, (3) varying facial expressions, and (4) varying pose. The system performance is also compared with the eigenface method, one of the best face recognition techniques, and with reported experimental results of other methods. A face pre-filtering technique is proposed to speed up the search process. It is a very encouraging to find that the proposed face recognition technique has performed better than the eigenface method in most of the comparison experiments. This research demonstrates that the LEM, together with the proposed generic line-segment Hausdorff distance measure, provides a new method for face coding and recognition     

Passive Three Dimensional Face Recognition Using Iso-Geodesic Contours and Procrustes Analysis

We introduce a new model for personal recognition based on the 3-D geometry of the face. The model is designed for application scenarios where the acquisition conditions constrain the facial position. The 3-D structure of a facial surface is compactly represented by sets of contours (facial contours) extracted around automatically pinpointed nose tip and inner eye corners. The metric used to decide whether a point on the face belongs to a facial contour is its geodesic distance from a given landmark. Iso-geodesic contours are inherently robust to head pose variations, including in-depth rotations of the face. Since these contours are extracted from rigid parts of the face, the resulting recognition algorithms are insensitive to changes in facial expressions. The facial contours are encoded using innovative pose invariant features, including Procrustean distances defined on pose-invariant curves. The extracted features are combined in a hierarchical manner to create three parallel face recognizers. Inspired by the effectiveness of region ensembles approaches, the three recognizers constructed around the nose tip and inner corners of the eyes are fused both at the feature-level and the match score-level to create a unified face recognition algorithm with boosted performance. The performances of the proposed algorithms are evaluated and compared with other algorithms from the literature on a large public database appropriate for the assumed constrained application scenario.     

Analysis of variance of Gabor filter banks parameters for optimal face recognition

Gabor filter banks constitute a very robust tool to extract discriminant information from a visual scene. After the now “classical” bank with 5 frequencies and 8 orientations proposed by Lades et al. and Wiskott et al., many other parametrizations of a Gabor filter bank have appeared. In order to find the optimal parametrization for a face recognition experiment, we have performed a 6-way analysis of variance of Gabor parameters using FERET, FRAV2D, FRAV3D, FRGC and XM2VTS face databases, including frontal and turned poses, facial expressions, occlusions and changes of illumination. Considering independent criteria to find the optimal Gabor filter bank, the bank with the highest recognition rate was found to have 6 frequencies and narrower Gaussian widths in the space domain. These results were obtained with Mahalanobis distance for a k-NN classifier, with analytical and holistic Gabor feature vectors. Moreover about 20% of the banks studied here obtained in average a better performance than the classical bank. For most of the databases considered, the highest recognition rates have been achieved with analytical representations (frontal images, images with turns or occlusions), with a holistic preponderance for images with gestures or changes of illumination. The inferiority found for holistic Gabor representations versus their analytical counterparts can be explained for the intrinsic redundancy and the size of the feature vectors of this kind of representation.