利用Hausdorff距离人脸图像定位算法

利用 Hausdorff距离的人脸图像定位算法是动态人脸识别系统的预处理工作 .定位算法与识别系统相结合 ,可提高人脸识别率 .由于动态人脸图像在移动过程中成像大小不是完全相同的 ,这样对进一步运动中人脸图像相互关系有较大的影响 ,提出了利用 Hausdorff距离来对图像中人脸进行定位 ,将图像中人脸部分提取出来并对其大小做调整 ,过滤掉背景信息 ,一定程度上简化了后续识别工作     

纹理豪斯多夫距离在人脸识别算法中的应用

很多经典的人脸识别方法难以适应姿势变化及人脸错位的情形。为了解决这一问题,提出一种基于纹理豪斯多夫距离(THD)的人脸识别算法。将人脸图像的空间量及纹理特征相结合,使其在深入的头部转动和人脸错位中都有很高的容错度。在FERET及Yale两大人脸数据库的实验表明,与其他经典的方法相比较,所提出的方法取得了更好的识别效果。     

一种基于Hausdorff距离的图像配准算法

首先检测两幅图像中的角点,然后自适应地提取基准特征模板,再利用改进的基于特征强度响应空间的Hausdorff距离对基准模板进行初始匹配,最后通过区域相关法进行优化.算法不要求特征间的一一对应,也无需距离变换,实验证明这是一种快速有效的图像配准算法.     

人脸图象的校准与特征抽取

脸自动识别是计算机模式识别领域的一个活跃课题,文中提出了一个基于眼睛位置估计的人脸校准方法,并提出了个基于ＫＬ变换与具有统计不相关性的最佳鉴别变换抽取人脸有效鉴别特征的方法,对由９６个,每个１０幅图象所组成的人脸图象数据库所作的实验结果表明,文中所提出的人脸校准方法能从具有一定复杂背景的人脸图象中比较成功地实现人脸自动校准,成功率与可靠性分别可达到９１．５％和９９．５％,文中所提出的人脸特征抽取     

基于P-ISOMAP特征脸谱的人脸识别

针对等距映射(Isometric Projection)算法计算量大且易受噪声影响的缺点,根据主成分分析(PCA)兼具降维和除噪的特性,提出一种基于ISOMAP算法的P-ISOMAP特征脸谱(PCA-ISOMAP)人脸识别技术。该算法首先通过主成分分析把人脸图映射到一个人脸图像子空间,然后在人脸图像子空间中进行等距映射降维。P-ISOMAP特征脸谱算法在保持人脸图像的全局几何特性和低维人脸流形的同时,提高了算法效率,能够有效抑制人脸图像中噪声。通过在人脸库中的实验表明,改进后的算法在识别率以及运行效率方面都优于等距映射算法。     

一种改进Hausdorff距离和谱聚类的车辆轨迹模式学习方法

为了对交通监控视频中的异常行为进行检测,需要对车辆的运动轨迹进行分析,但由于噪声、遮挡等原因,不可能获得完整的运动轨迹,导致分析结果不准确。针对此类问题,提出基于改进Hausdorff距离和谱聚类的轨迹聚类方法,首先对提取到的轨迹进行预处理,然后利用改进的Hausdorff距离进行轨迹相似度度量,最后通过谱聚类方法对距离矩阵进行聚类,得到符合实际情况的聚类结果。实验结果表明,该方法具有较好的鲁棒性和有效性。     

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.     

人脸自动检测与识别技术的研究及应用

本文提出了一种基于可疑人脸区域发现算法和结合投影图的人脸识别算法,算法在标准的人脸库上进行了实验,实验结果表明该算法能在人脸库中正确检测到绝大多数人脸的位置,并且在识别过程中体现了比标准特征脸方法更高的识别率和更小的计算量。以此算法为基础,研制和开发了一种计算机自动考勤系统。     

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.     

一种人脸识别中眼睛定位算法

本文介绍了一种灰色图像识别中进行人眼睛定位的算法,首先是根据人脸的水平灰度直方图来确定艰睛的位置,然后,通过计算眼眶部分像素点的垂直灰度直方图来确定两眼中心,接着从获得的区域中找出一定数量的灰度值小的点,对这些点进行添加、删除和计算,实现眼睛的定位。     

一种解决人脸识别误匹配灾难问题的方法

人脸识别过程中由于外界环境、样本数量以及扰动条件等因素的影响,分类器的分类错误率会出现急剧增加,导致误匹配灾难现象的出现。针对这一问题,采用一种改进型感知器与预识别算法,在人脸无拒识闭集识别的基础上改善和解决了这一问题。在VC++平台设计出的实时人脸识别系统上经过多次测试,结果表明,所设计的分类算法具有很好的自适应性与鲁棒性,误匹配率低、速度快,便于实际应用。     

人脸图像的自动识别技术

本文讨论了人脸识别技术（FRT）中分割、特征提取及识别的有关方法,阐明了各种方法的基本思想、使用范围及优缺点,并给出了相应的计算公式,最后指出了对这些方法的性能进行定量评估所面临的问题。     

基于边缘检测的人脸模拟画像检索

为配合刑事侦查工作和公安网上追逃的迫切需要,对人脸模拟画像的检索作初步研究.首先,根据网上追逃数据库中的人脸照片设计出一种半自动的归一化算法.其次,研究了人脸模拟画像和人脸照片的边缘检测方法,给出一种自适应的人脸照片边缘检测算法,从而得到三种用于人脸模拟画像检索的统计特征.最后,建立了人脸模拟画像检索的实验数据库,给出相似性量度,并对实验数据进行了分析.为构建实用的人脸模拟画像检索系统的开发提供了理论支持;对金盾工程的完善起到良好的促进作用.     

融合约束采样和面部对齐的稀疏表示人脸识别

针对传统的稀疏表示分类算法中面部对齐受限而影响人脸识别率的问题,提出一种基于约束采样和面部对齐的稀疏表示分类算法。首先通过使用约束采样对训练图像进行预先标注得到固定脸特征;然后结合图像的纹理信息和形状特征进行面部对齐及特征提取;最后计算出测试样本与各个训练样本之间的相似度,利用稀疏表示分类器完成人脸的识别。在AR、CAS-PEAL及扩展YaleB人脸数据库上的实验验证了算法的有效性及鲁棒性。实验结果表明,约束采样和面部对齐的组合大大提高了人脸识别率,相比几种较为先进的鲁棒人脸识别算法,该算法取得了更好的识别效果。     

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.     

Further results on the subspace distance

In previous papers [L. Wang, X. Wang, J. Feng, Subspace distance analysis with application to adaptive Bayesian algorithm for face recognition, Pattern Recognition 39(3) (2006) 456-464, L. Wang, X. Wang, J. Feng, Intrapersonal subspace analysis with application to adaptive Bayesian algorithm for face recognition, Pattern Recognition 38(4) (2005) 617-621], we proposed a subspace distance. However, whether the subspace distance satisfies the triangle inequality was left open. In this note, we give positive answer to the open problem and prove our assertion.     

基于改进的WLD特征的人脸识别

对WLD特征进行改进。改进的特征提取方法为:首先将原始人脸图像划分为若干个子块,然后提取每块图像的WLD直方图统计特征,其中的梯度方向是用Prewitt算子计算的,最后将所有分块的WLD直方图序列连接起来构成特征向量。为了验证改进特征的性能,用支持向量机进行人脸识别,人脸图像取自ORL和YALE人脸数据库。实验结果表明,采用改进后的特征可以显著提高人脸识别率。     

一种稳健的人脸检测方法

人脸检测作为人脸识别的第一环节,也是一个模式识别问题,本文提出了一种稳健的复杂背景下脸检测方法,并对人脸图象库ＦＤＢ６０３进行了实验,结果表明,我们的方法是有效和稳定的。     

正则化双阶线性稀疏编码在人脸识别中的应用

传统的稀疏编码方法在遇到大规模数据时,因计算复杂度高而出现异常。针对这种异常导致不能很好地进行特征提取的问题,提出正则化双阶线性稀疏编码DLRSC(Double Linear Regularization Sparse Coding)方法。借助于广义多特征子空间框架来学习噪声和异常像素的结构特征,通过使用L1球理论,计算出唯一的近似解,并且利用滤波技巧避免了大规模数据的复杂计算,从而降低了时间及空间复杂度。最后,在ORL及Yale两大通用人脸数据库上的实验验证了所提的DLRSC方法的有效性,实验结果表明,相比其他几种最先进的稀疏编码方法,所提方法取得了更好的识别效果。