Spatially eigen-weighted Hausdorff distances for human face recognition

Hausdorff distance is an efficient measure of the similarity of two point sets. In this paper, we propose a new spatially weighted Hausdorff distance measure for human face recognition. The weighting function used in the Hausdorff distance measure is based on an eigenface, which has a large value at locations of importance facial features and can reflect the face structure more effectively. Two modified Hausdorff distances, namely, “spatially eigen-weighted Hausdorff distance” (SEWHD) and “spatially eigen-weighted ‘doubly’ Hausdorff distance” (SEW2HD) are proposed, which incorporate the information about the location of important facial features such as eyes, mouth, and face contour so that distances at those regions will be emphasized. Experimental results based on a combination of the ORL, MIT, and Yale face databases show that SEW2HD can achieve recognition rates of 83%, 90% and 92% for the first one, the first three and the first five likely matched faces, respectively, while the corresponding recognition rates of SEWHD are 80%, 83% and 88%, respectively.     

Line segment Hausdorff distance on face matching

A novel concept of line segment Hausdorff distance is proposed in this paper. Researchers apply Hausdorff distance to measure the similarity of two point sets. It is extended here to match two sets of line segments. The new approach has the advantage to incorporate structural and spatial information to compute the similarity. The added information can conceptually provide more and better distinctive capability for recognition. This would strengthen and enhance the matching process of similar objects such as faces. The proposed technique has been applied online segments generated from the edge maps of faces with encouraging result that supports the concept experimentally. The results also implicate that line segments could provide sufficient information for face recognition. This might imply a new way for face coding and recognition.     

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     

基于信息测度特征和Hausdorff距离的图像匹配策略

由于传统的图像匹配方法受到诸如景物的遮挡、光照和噪声的影响比较大，并且需要建立模板与图像间的对应关系，因而使实际图像的匹配变得困难。为了克服上述缺陷，以便快速地进行图像匹配，通过引入信息测度概念来提取边缘特征点，并基于修正后的：Hausdorff距离构造相似性测度，提出了一种基于信息测度和：Hausdorff距离的图像匹配策略。该策略不仅加快了匹配过程，提高了抗噪性能，而且能准确匹配遮挡图像，从而较好地解决了基于传统Hausdorff距离的图像匹配因噪声点、伪边缘和出格点而造成的误匹配问题。实验结果证明，该方法是正确有效的。     

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.     

Hybrid image matching combining Hausdorff distance with normalized gradient matching

Image matching has been a central problem in computer vision and image processing for decades. Most of the previous approaches to image matching can be categorized into the intensity-based and edge-based comparison. Hausdorff distance has been widely used for comparing point sets or edge maps since it does not require point correspondences. In this paper, we propose a new image similarity measure combining the Hausdorff distance with a normalized gradient consistency score for image matching. The normalized gradient consistency score is designed to compare the normalized image gradient fields between two images to alleviate the illumination variation problem in image matching. By combining the edge-based and intensity-based information for image matching, we are able to achieve robust image matching under different lighting conditions. We show the superior robustness property of the proposed image matching technique through experiments on face recognition under different lighting conditions.     

基于外耳轮廓边缘信息的人耳识别

提出一种用Hausdorff距离表示入耳边缘特征的人耳识别方法.首先对原始人耳图像进行预处理,用基于灰度形态学梯度和局部阈值分割的边缘检测方法提取外耳轮廓边缘;然后用标准方差和边缘线段间长度差改进的Hausdorff距离表示人耳特征向量;最后采用支持向量机算法完成人耳识别.实验结果证明,该方法能获得更高的人耳识别率.     

Robust Hausdorff distance measure for face recognition

Face is considered to be one of the biometrics in automatic person identification. The non-intrusive nature of face recognition makes it an attractive choice. For face recognition system to be practical, it should be robust to variations in illumination, pose and expression as humans recognize faces irrespective of all these variations. In this paper, an attempt to address these issues is made using a new Hausdorff distance-based measure. The proposed measure represent the gray values of pixels in face images as vectors giving the neighborhood intensity distribution of the pixels. The transformation is expected to be less sensitive to illumination variations besides preserving the appearance of face embedded in the original gray image. While the existing Hausdorff distance-based measures are defined between the binary edge images of faces which contains primarily structural information, the proposed measure gives the dissimilarity between the appearance of faces. An efficient method to compute the proposed measure is presented. The performance of the method on bench mark face databases shows that it is robust to considerable variations in pose, expression and illumination. Comparison with some of the existing Hausdorff distance-based methods shows that the proposed method performs better in many cases.     

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。     

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。     

一种基于鲁棒Hausdorff距离的目标匹配算法

在传统的基于边缘位置的Hausdorff距离匹配的基础上,将边缘的梯度信息引入到距离度量当中,构造了一种新的三维距离函数。在此基础上,提出了一种鲁棒的三维Hausdorff距离及其目标匹配算法,采用粗匹配与精匹配相结合的两步匹配策略有效解决了由距离度量维数增加所导致的算法复杂性增大的问题。实验表明,该算法相对于传统的基于边缘位置的Hausdorff距离目标匹配算法在鲁棒性上有很大的提高。     

一种特征加权融合人脸识别方法

针对传统人脸识别算法在非限制条件下识别准确率不高的问题,提出了一种特征加权融合人脸识别方法(DLWF⁺)。根据人脸面部左眼、右眼、鼻子、嘴、下巴等5个器官位置,将人脸图像划分成5个局部采样区域;将得到的5个局部采样区域和整幅人脸图像分别输入到对应的神经网络中进行网络权值调整,完成子网络的构建;利用softmax回归求出6个相似度向量并组成相似度矩阵与权向量相乘得出最终的识别结果。经ORL和WFL人脸库上进行实验验证,识别准确率分别达到97%和91.63%。实验结果表明:该算法能够有效提高人脸识别能力,与传统识别算法相比在限制条件和非限制条件下都具有较高的识别准确率。     

基于深度神经网络的特征加权融合人脸识别方法

针对目前难以提取到适合用于分类的人脸特征以及在非限条件下进行人脸识别准确率低的问题,提出了一种基于深度神经网络的特征加权融合人脸识别方法(DLWF)。首先,应用主动形状模型(ASM)提取出人脸面部的主要特征点,并根据主要特征点对人脸不同器官区域进行采样;然后,将所得采样块分别输入到对应的深度信念网络(DBN)中进行训练,获得网络最优参数;最后,利用Softmax回归求出各个区域的相似度向量,将多区域的相似度向量加权融合得到综合相似度评分进行人脸识别。经ORL和WFL人脸库上进行实验验证,DLWF算法的识别准确率分别达到97%和88.76%,与传统算法主成分分析(PCA)、支持向量机(SVM)、DBN及FIP+线性判别式分析(LDA)相比,无论是限制条件还是非限制条件下,识别率均有提高。实验结果表明,该算法具有高效的人脸识别能力。     

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.     

Efficient 3D face recognition handling facial expression and hair occlusion

This paper presents an efficient 3D face recognition method to handle facial expression and hair occlusion. The proposed method uses facial curves to form a rejection classifier and produce a facial deformation mapping and then adaptively selects regions for matching. When a new 3D face with an arbitrary pose and expression is queried, the pose is normalized based on the automatically detected nose tip and the principal component analysis (PCA) follows. Then, the facial curve in the nose region is extracted and used to form the rejection classifier which quickly eliminates dissimilar faces in the gallery for efficient recognition. Next, six facial regions which cover the face are segmented and curves in these regions are used to map facial deformation. Regions used for matching are automatically selected based on the deformation mapping. In the end, results of all the matching engines are fused by weighted sum rule. The approach is applied on the FRGC v2.0 dataset and a verification rate of 96.0% for ROC III is achieved as a false acceptance rate (FAR) of 0.1%. In the identification scenario, a rank-one accuracy of 97.8% is achieved.     

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.     

Gray Hausdorff distance measure for comparing face images

Human face recognition is considered to be one of the toughest problems in the domain of pattern recognition. The variations in face images due to differing expression, pose and illumination are some of the key issues to be addressed in developing a face recognition system. In this paper, a new measure called gray Hausdorff distance (denoted by H/sub pg/) is proposed to compare the gray images of faces directly. An efficient algorithm for computation of the new measure is presented. The computation time is linear in the size of the image. The performance of this measure is evaluated on benchmark face databases. The face recognition system based on the new measure is found to be robust to pose and expression variations, as well as to slight variation in illumination. Comparison studies show that the proposed measure performs better than the existing ones in most cases.     

基于ASM和线段Hausdorff距离的人脸识别

主动形状模型（ASM）是人脸特征定位的有效方法。针对ASM的不足,从初始定位、建模方法和搜索策略等三个方面进行了改进,提出了基于模块化ASM(MASM)的定位方法。实验结果表明,改进后的方法在定位准确度上有了较大提高。此外,利用模块化ASM定位得到的人脸轮廓及各器官的形状特征,采用线段Hausdorff距离(LHD)作为相似性测度,在CVL人脸数据库上进行人脸识别,获得了较好的识别效果。