Web前端开发技术研究

阐述Web前端开发相关技术等,主要聚焦于基于Web浏览器编程的前端开发技术和标准,内容包括HTML/XHTML、层叠样式表(CSS)、文档对象模型(DOM)、JavaScript、跨浏览器开发、Firefox扩展开发以及Web站点设计与维护等。     

基于AdaBoost的人脸特征挑选与特征融合算法研究

针对人脸识别中的特征挑选和特征融合问题进行研究。结合已有的基于AdaBoost的人脸特征挑选方法,挑选出最具分类能力的特征,并将挑选出的多类人脸特征在特征层进行融合,得到一个统一的人脸特征用于模式分类。通过在FERET人脸库上的实验表明,其识别方法具有良好的识别效果。     

基于BP神经网络的人脸识别研究

为了对人脸图像的特征向量进行分类以达到人脸识别的目的,本文提出了运用BP神经网络进行人脸识别的方法。将人脸图像矩阵的奇异值作为识别特征,将BP神经网络作为分类器,通过实验表明该方法操作性强,结果可靠,可以快速的进行人脸图像识别。     

基于肤色特征的人脸检测算法的研究

通过对比面部识别技术的各种算法,本文重点研究了基于肤色特征的人脸检测算法。首先通过两种方法实现人脸的区域分割：基于相似度的方法和基于皮肤区域、头发区域的方法。前者通过相似度计算、二值化之后标记出人脸区域,后者利用颜色来检测人脸区域。人脸区域检测完毕之后再对其进行人脸特征的标注,最终实现面部检测。     

三维人脸识别研究

对人脸识别进行了简单的描述,然后对人脸识别的传统及新近技术进行技术与应用的介绍。首先介绍人脸识别的概念及其发展历史,随后对人脸识别技术方法发展过程中一些传统方法进行阐述,继而对新近发展的新技术进行了探讨;最后介绍人脸识别技术的应用现状。     

基于SVM的人脸检测研究

支持向量机（SVM）是在统计学习理论基础上发展起来的一种新的机器学习算法。依据SVM良好识别和泛化能力,实现了一种基于支持向量机的图像人脸识别方法。利用Opencv提取样本类的低层特征,训练具有径向基核函数的SVM分类器,在VS2008和Qt平台下实现识别软件开发。运行结果表明,软件具有良好的图像人脸检测能力。     

基于图像矩的驾驶员面部椭圆拟合定位

基于人脸类似椭圆的特性,提出一种基于图像矩的驾驶员面部椭圆拟合定位方法.该方法利用肤色信息对驾驶室采集的彩色图像在颜色空间进行快速分割的基础上,利用图像矩拟合驾驶员面部椭圆,从而准确的定位跟踪驾驶员的位置,有效的降低了驾驶室的复杂背景、驾驶员各种面部姿态、表情对人脸检测定位的影响,同时也避免了常用椭圆拟合方法计算量大的不足.实验与仿真表明该方法具有快速、稳健和姿态不敏感的特性.     

基于谱直方图与支持向量机的人脸检测

提出了一种基于谱直方图和支持向量机的人脸检测算法。首先使用梯度滤波器、LoG滤波器和LBP算子计算图像的谱直方图,然后使用支持向量机进行分类。经实验表明,算法可以克服光照、姿势、表情、眼镜等干扰,并允许有局部遮挡,具有较强的鲁棒性。     

Face recognition using adaptively weighted patch PZM array from a single exemplar image per person

Though numerous approaches have been proposed for face recognition, little attention is given to the moment-based face recognition techniques. In this paper we propose a novel face recognition approach based on adaptively weighted patch pseudo Zernike moment array (AWPPZMA) when only one exemplar image per person is available. In this approach, a face image is represented as an array of patch pseudo Zernike moments (PPZM) extracted from a partitioned face image containing moment information of local areas instead of global information of a face. An adaptively weighting scheme is used to assign proper weights to each PPZM to adjust the contribution of each local area of a face in terms of the quantity of identity information that a patch contains and the likelihood of a patch is occluded. An extensive experimental investigation is conducted using AR and Yale face databases covering face recognition under controlled/ideal conditions, different illumination conditions, different facial expressions and partial occlusion. The system performance is compared with the performance of four benchmark approaches. The encouraging experimental results demonstrate that moments can be used for face recognition and patch-based moment array provides a novel way for face representation and recognition in single model databases.     

Locally linear discriminant embedding: An efficient method for face recognition

In this paper an efficient feature extraction method named as locally linear discriminant embedding (LLDE) is proposed for face recognition. It is well known that a point can be linearly reconstructed by its neighbors and the reconstruction weights are under the sum-to-one constraint in the classical locally linear embedding (LLE). So the constrained weights obey an important symmetry: for any particular data point, they are invariant to rotations, rescalings and translations. The latter two are introduced to the proposed method to strengthen the classification ability of the original LLE. The data with different class labels are translated by the corresponding vectors and those belonging to the same class are translated by the same vector. In order to cluster the data with the same label closer, they are also rescaled to some extent. So after translation and rescaling, the discriminability of the data will be improved significantly. The proposed method is compared with some related feature extraction methods such as maximum margin criterion (MMC), as well as other supervised manifold learning-based approaches, for example ensemble unified LLE and linear discriminant analysis (En-ULLELDA), locally linear discriminant analysis (LLDA). Experimental results on Yale and CMU PIE face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.