Fast and robust face recognition via coding residual map learning based adaptive masking

Robust face recognition (FR) is an active topic in computer vision and biometrics, while face occlusion is one of the most challenging problems for robust FR. Recently, the representation (or coding) based FR schemes with sparse coding coefficients and coding residual have demonstrated good robustness to face occlusion; however, the high complexity of l₁-minimization makes them less useful in practical applications. In this paper we propose a novel coding residual map learning scheme for fast and robust FR based on the fact that occluded pixels usually have higher coding residuals when representing an occluded face image over the non-occluded training samples. A dictionary is learned to code the training samples, and the distribution of coding residuals is computed. Consequently, a residual map is learned to detect the occlusions by adaptive thresholding. Finally the face image is identified by masking the detected occlusion pixels from face representation. Experiments on benchmark databases show that the proposed scheme has much lower time complexity but comparable FR accuracy with other popular approaches.     

Multi-resolution feature fusion for face recognition

For face recognition, image features are first extracted and then matched to those features in a gallery set. The amount of information and the effectiveness of the features used will determine the recognition performance. In this paper, we propose a novel face recognition approach using information about face images at higher and lower resolutions so as to enhance the information content of the features that are extracted and combined at different resolutions. As the features from different resolutions should closely correlate with each other, we employ the cascaded generalized canonical correlation analysis (GCCA) to fuse the information to form a single feature vector for face recognition. To improve the performance and efficiency, we also employ “Gabor-feature hallucination”, which predicts the high-resolution (HR) Gabor features from the Gabor features of a face image directly by local linear regression. We also extend the algorithm to low-resolution (LR) face recognition, in which the medium-resolution (MR) and HR Gabor features of a LR input image are estimated directly. The LR Gabor features and the predicted MR and HR Gabor features are then fused using GCCA for LR face recognition. Our algorithm can avoid having to perform the interpolation/super-resolution of face images and having to extract HR Gabor features. Experimental results show that the proposed methods have a superior recognition rate and are more efficient than traditional methods.     

综采工作面初采初放期间矿压显现规律分析与总结

2903综采工作面是城郊煤矿二水平第一个单顺槽沿空掘巷综采工作面。项目针对该工作面初采、初放期间的矿压显现规律进行了数据采集、分析和总结。     

联合 LTP 和 LPQ 的人脸识别方法

提出一种联合局部三值模式（LTP）和局部相位量化（LPQ）的人脸识别方法,该方法首先对预处理后的人脸分别采用LTP算子、LPQ算子提取各自的直方图,然后,通过特征选择将两者联合为LTP＿LPQ直方图,最后通过最近邻分类器进行分类识别。在ORL和YALE标准人脸数据库上的实验表明,该方法能有效地提高人脸的识别率。     

Adaptive Face Recognition via Structed Representation

In this paper, we propose a face recognition approach-Structed Sparse Representation-based classification when the measurement of the test sample is less than the number training samples of each subject. When this condition is not satisfied, we exploit Nearest Subspaee approach to classify the test sample. In order to adapt all the eases, we combine the two approaches to an adaptive classification method-Adaptive approach. The adaptive approach yields greater recognition accuracy than the SRC approach and CRC_RLS approach with low ~ample rate on the Extend Yale B dataset. And it is more efficient than other two approaches.     

稀疏表示分类中遮挡字典构造方法的改进

针对稀疏表示分类算法中遮挡字典维数高且无冗余的问题,提出一种遮挡字典构造方法.首先通过图像分块得到各级的遮挡基图像;然后将所有互不相同的遮挡基图像按字典顺序转化为向量,并用这些向量作为遮挡字典的列,从而构造出维数相对较低且具有一定冗余度的遮挡字典.实验结果表明,该方法不仅明显提高了稀疏表示分类算法对遮挡人脸的识别率,而且还能通过减少图像的分块级数降低稀疏分解的耗时量,提高运算效率.     

基于优化支持向量机的人脸表情分类

分析了支持向量机（support vector machine, SVM）目前主要存在的问题和参数选择对分类性能的影响后, 提出了以改进粒子群算法优化SVM关键参数的优化SVM算法。将加入拥挤度因子的微粒群算法引入到SVM中, 在不牺牲泛化性能的前提下, 对其参数进行优化, 增加了SVM初始化参数的多样性, 减慢了局部搜索, 促进其在全局范围内的寻优搜索, 以有效克服SVM算法过分依赖初始值和容易陷入局部极小值的缺点, 并利用由粗到精的策略构造多层SVM人脸表情分类器, 在提高准确率的基础上加快分类的速度。实验证明, 新算法具有速度快、准确率高的优点。     

基于组合方法的人脸识别算法研究

该文试探一种加权融合流形学习的方法进行人脸识别,该算法通过Haar小波和局部线性嵌入（LLE）加权融合的方式来进行人脸识别。首先通过Haar小波变换对人脸图像进行一级分解,得到四个子图;然后利用LLE算法对四个子图降维处理,并加权融合;最后利用支持向量机（SVM）进行分类判决。通过在ORL库中进行实验,结果表明,该文提出的Haar＋LLE识别效率比单独使用主成分分析（PCA）和LLE更高效。     

基于形变模型的多视图三维人脸重建

本文提出了基于形变模型的多视图三维人脸重建方法,将人脸形变模型与同一人脸在不同视点下的多幅图像进行匹配,从而重建出具有较强真实感的三维人脸模型。本文将对基于形变模型的多视图三维人脸重建方法进行详细的阐述,并把实验结果与单视图重建出的三维人脸模型进行了对比,从而体现出多视图重建的优势所在。     

基于遗传算法的人脸识别系统设计

该文设计了一套基于遗传算法的人脸识别系统,整个系统包括人脸检测与定位阶段,该文采用垂直灰度积分投影算法确定了人脸左右边界,采用基于Hough变换的圆检测算法定位了眼睛中心,然后利用小范围水平灰度积分投影法确定了嘴巴中心,最后利用方框锁定、局部搜索法确定了眼角和嘴角。特征提取阶段,该文在特征矢量的质量与数量两方面做了权衡,保证了系统的运行速度。人脸识别阶段,该文采用了根据遗传学而演化的一种高精度、高搜索度算法——遗传算法训练并确定了识别系统的权重与阈值,提高了识别系统的速度与精度。在系统设计中,该文选用了两个人脸数据库共计180幅图像用于训练与检测,该系统在实验中识别正确率可达95%以上。