熵权法融合局部匹配Gabor特征的鲁棒人脸识别

针对复杂环境下人脸识别难度大的问题,提出了一种熵权法融合局部Gabor特征方法。计算类熵加权向量;计算局部归一化输入图像的Borda计数矩阵,从而消除低值Gabor jet比较矩阵;通过将分数层类熵加权Gabor特征与LGBP和LGXP融合解决了完成人脸的识别。在FERET、AR和FRGC 2.0人脸数据库上的实验结果表明,该方法对轻微姿态变化具有显著鲁棒性,并且对人眼检测中高达3像素的误差具有鲁棒性,相比其他几种人脸识别方法,该方法取得了更好的识别效果。     

基于Gabor不确定度的嵌入式人脸识别系统

多尺度Gabor特征的维数和数据量过大,不适合在ARM板上直接实现完成。利用计算每个尺度Gabor特征不确定度并采用加权融合的方法,很好地解决了图像维数和数据量过大的难点。加权融合过程包括多尺度Gabor特征的提取、不确定度权值的计算和加权融合过程;同时使用了类Haar特征提取人脸、利用二维主成分分析(2DPCA)对人脸图像进行降维。基于EELiod 270嵌入式开发平台,使用ORL和Yale图像库对该方法进行了测试,并与其他人脸识别算法进行比较。结果显示,在保证识别率的同时,算法运算量大幅度下降,且实时识别效果良好。     

Methodological improvement on local Gabor face recognition based on feature selection and enhanced Borda count

Face recognition has a wide range of possible applications in surveillance, human computer interfaces and marketing and advertising goods for selected customers according to age and gender. Because of the high classification rate and reduced computational time, one of the best methods for face recognition is based on Gabor jet feature extraction and Borda count classification. In this paper, we propose methodological improvements to increase face recognition rate by selection of Gabor jets using entropy and genetic algorithms. This selection of jets additionally allows faster processing for real-time face recognition. We also propose improvements in the Borda count classification through a weighted Borda count and a threshold to eliminate low score jets from the voting process to increase the face recognition rate. Combinations of Gabor jet selection and Borda count improvements are also proposed. We compare our results with those published in the literature to date and find significant improvements. Our best results on the FERET database are 99.8%, 99.5%, 89.2% and 86.8% recognition rates on the subsets Fb, Fc, Dup1 and Dup2, respectively. Compared to the best results published in the literature, the total number of recognition errors decreased from 163 to 112 (31%). We also tested the proposed method under illumination changes, occlusions with sunglasses and scarves and for small pose variations. Results on two different face databases (AR and Extended Yale B) with significant illumination changes showed over 90% recognition rate. The combination EJS-BTH-BIP reached 98% and 99% recognition rate in images with sunglasses and scarves from the AR database, respectively. The proposed method reached 93.5% recognition on faces with small pose variation of 25° rotation and 98.5% with 15% rotation in the FERET database.     

Gabor特征融合的嵌入式人脸识别系统的研究

在嵌入式人脸识别系统中,由于多尺度Gabor抽取特征的维数和数据量过大,不适合在ARM板上实现,提出了多尺度Gabor特征加权融合的方法,很好地解决了图像维数和数据量过大的难点。加权融合过程包括多尺度Gabor特征的提取、特征权值的计算和加权融合过程。同时使用了类Haar特征提取人脸、2DPCA对人脸图像进行降维。基于EELiod 270嵌入式开发平台实现了一个嵌入式系统,结合典型图片库和实际图片进行了人脸识别测试,实践结果表明,系统在保证一定的识别率的同时,大幅度降低了运行时间,实时识别效果良好。     

基于Gabor滤波器的快速人脸识别算法

针对传统人脸识别方法中所提取特征维数高、计算量大等缺点,提出一种新的正面人脸识别算法。新算法融合了半边人脸识别方法、Gabor滤波器、基于互信息判据的Gabor特征筛选来进行人脸识别。新算法将人脸图像分为左右两个部分,计算并比较人脸图像左右半边脸的熵,选取熵值较大的半边人脸图像进行Gabor特征提取。利用二值分类器判别单个Gabor特征的分类能力,选取分类能力较强的特征（最具判决力的特征）。再利用互信息判据对Gabor特征进行第二次筛选,以减小特征之间的冗余度。最后利用最近邻判别器来进行人脸识别。实验结果表明,新算法的识别率优于传统半边脸识别方法,识别速度也优于传统的利用Gabor滤波器进行特征提取的方法。     

Face recognition based on fusion of multi-resolution Gabor features

In this paper, we propose a Gabor-based face recognition method. This method fuses multi-resolution Gabor features of face images at the matching score level. The first implementation scheme of this method directly takes the sum of the matching scores of multi-resolution Gabor features of face images as the final matching score. The second implementation scheme first codes the phase of the Gabor feature and then uses a weighted matching score level fusion algorithm to fuse the magnitude and phase of the Gabor feature. A number of experimental results show that the proposed method has a good performance and outperforms conventional Gabor-based face recognition methods that equally treat all the Gabor features and directly fuse them at the feature level. The experimental result also illustrates that in face recognition, the low-resolution representation of the phase of the Gabor feature such as the code of the phase is more discriminative than the phase itself. The codes of our method will be available at http://www.yongxu.org/lunwen.html.     

A review on Gabor wavelets for face recognition

Due to the robustness of Gabor features against local distortions caused by variance of illumination, expression and pose, they have been successfully applied for face recognition. The Facial Recognition Technology (FERET) evaluation and the recent Face Verification Competition (FVC2004) have seen the top performance of Gabor feature based methods. This paper aims to give a detailed survey of state of the art 2D face recognition algorithms using Gabor wavelets for feature extraction. Existing problems are covered and possible solutions are suggested.     

基于Gabor小波和SVD的热红外人脸识别研究

由于热红外人脸图像具有防伪装、防欺诈以及独立于环境光照的特点,所以近年来热红外人脸识别问题备受关注。提出一种基于Gabor小波和SVD的热红外人脸识别新方法。对归一化后的热红外人脸图像进行多方向多尺度Gabor变换,得到多个Gabor特征矩阵;对每个矩阵进行奇异值分解,并把每个矩阵最大的奇异值组合起来作为最终的热红外人脸特征向量;使用径向基神经网络进行分类识别。在自建热红外人脸数据库上的实验结果表明,相比于传统的识别方法,该方法具有较好的识别效果。     

基于分块Gabor特征的贝叶斯人脸识别

对贝叶斯分类中最大似然（ML）公式进行了简化,给出了一种实用的快速计算相似度的方法,在此基础上设计了基于分块Gabor特征提取的贝叶斯人脸识别算法。该算法从原始数字图像出发,先对图像矩阵进行分块,然后对分块子图像进行多分辨率的Gabor特征提取,对每一个特征块设计一个贝叶斯分类器,通过将这些分类器加权平均,得到最后的决策。在FERET人脸数据库的实验结果验证了该方法的有效性。     

Gabor wavelet associative memory for face recognition

This letter describes a high-performance face recognition system by combining two recently proposed neural network models, namely Gabor wavelet network (GWN) and kernel associative memory (KAM), into a unified structure called Gabor wavelet associative memory (GWAM). GWAM has superior representation capability inherited from GWN and consequently demonstrates a much better recognition performance than KAM. Extensive experiments have been conducted to evaluate a GWAM-based recognition scheme using three popular face databases, i.e., FERET database, Olivetti-Oracle Research Lab (ORL) database and AR face database. The experimental results consistently show our scheme's superiority and demonstrate its very high-performance comparing favorably to some recent face recognition methods, achieving 99.3% and 100% accuracy, respectively, on the former two databases, exhibiting very robust performance on the last database against varying illumination conditions.     

Inverse transformation based weighted fusion for face recognition

Multimedia Tools and Applications - Score fusion is a promising approach in face recognition. This paper proposes an algorithm that can integrate original images and virtual images. We obtain...     

Simplified Gabor wavelets for human face recognition

Gabor wavelets (GWs) are commonly used for extracting local features for various applications such as object detection, recognition and tracking. However, extracting Gabor features is computationally intensive, so the features are impractical for real-time applications. In this paper, we propose a simplified version of Gabor wavelets (SGWs) and an efficient algorithm for extracting the features based on an integral image. We evaluate the performance of the SGW features for face recognition. Experimental results show that using SGWs can achieve a performance level similar to using GWs, while the runtime for feature extraction using SGWs is, at most, 4.39 times faster than that of GWs implemented by using the fast Fourier transform (FFT).     

基于Gabor小波变换的人脸疲劳模式识别

疲劳是造成交通事故的主因之一,提出了一种基于Gabor小波变换的疲劳监控新方法。首先,在训练阶段采用频繁模式挖掘算法对疲劳脸部图像序列集进行疲劳模式挖掘;然后,在疲劳识别阶段,将待检测的脸部图像序列基于Gabor小波变换表示为融合特征序列;最后,采用分类算法进行人脸序列的疲劳检测。对自行收集的一天内500幅疲劳图像的仿真结果表明,所提方法正确检测率达到92.8%,错误检测率达到0.02%,优于比较算法。     

Optimal fusion aided face recognition from visible and thermal face images

Multimedia Tools and Applications - This paper proposes a novel Eigen face recognition that is aided by fusion of visible and thermal face images to improve the face recognition accuracy. We adopt...     

基于融合的显微图像Gabor边缘提取算法

不同参数Gabor滤波器都具有各自的频率选择和方向选择特性,图像中纹理基元可以利用多个方向和中心频率Gabor滤波器组提取出来的频谱值来表示.据此提出一种适应于显微图像的Gabor滤波边缘检测算法.使用特定不同方向Gabor滤波边缘提取算子对图像进行边缘信息提取,获得不同方向上边缘特征信息,对其进行分析和融合提取图像边缘.运用该方法时所获不同方向的图像边缘进行自适应融合,获得的图像边缘较理想,模糊的边缘得到增强,并有效地消除了噪声.实验结果表明,该算法对显微图像处理有效,检测到的边缘清晰.     

独立环形对称Gabor特征及在人脸识别中的应用

提出了一种基于2D环形对称Gabor变换（2DCSGT）和独立成分分析（ICA）的特征提取方法,并用于人脸识别中。对人脸图像做5尺度2D环形对称Gabor变换;对变换后的图像下采样并采用主成分分析（PCA）进行降维;采用ICA获得人脸图像的独立环形对称Gabor特征（ICSGF）;采用最近邻分类法分类。在ORL、YALE和FERET人脸数据库上,基于ICSGF的人脸识别算法的正确识别率分别达到99.5%、93.33%和97.14%。实验结果表明,ICSGF方法有效可行。     

融合Gabor和Gist特征的人脸识别

为了进一步增强人脸识别系统的实用性,提高人脸识别率,提出了一种新的融合Gabor小波特征和Gist特征的人脸特征提取方法。对一幅人脸图像提取其多个尺度和方向的Gabor特征图,再对这些Gabor特征图进行处理,分别提取其Gist特征,接着再把所有Gabor特征图的Gist特征级联起来作为一人脸图像的特征向量,经过PCA方法降维处理,最后输入到支持向量机里面训练识别。通过在ORL和FERET人脸库中进行实验检测,结果表明与传统的PCA-SVM方法和Gabor特征提取方法相比,给出的方法可以大幅度提高人脸识别率。     

基于对比学习的多特征融合戴口罩人脸识别

随着计算机视觉技术应用的发展和智能终端的普及,口罩遮挡人脸识别已成为人物身份信息识别的重要部分。口罩的大面积遮挡对人脸特征的学习带来极大挑战。针对戴口罩人脸特征学习困难这一问题,提出了一种基于对比学习的多特征融合口罩遮挡人脸识别算法,该算法改进了传统的基于三元组关系的人脸特征向量学习损失函数,提出了基于多实例关系的损失函数,充分挖掘戴口罩人脸和完整人脸多个正负样本之间的同模态内和跨模态间的关联关系,学习人脸中具有高区分度的能力的特征,同时结合人脸的眉眼等局部特征和轮廓等全局特征,学习口罩遮挡人脸的有效特征向量表示。在真实的戴口罩人脸数据集和生成的戴口罩人脸数据上与基准算法进行了比较,实验结果表明所提算法相比传统的基于三元组损失函数和特征融合算法具有更高的识别准确率。     

基于局部人脸图像的ICA人脸识别方法

提出了一种基于局部人脸图像独立分量分析的特征提取方法.该方法将人脸图像分成若干个相等的部分,将分成的局部人脸图像矩阵作为训练样本,并先后从水平方向,垂直方向提取训练样本的独立分量.相较于传统的独立分量分析(ICA)方法,该方法具有如下优点:有效解决了传统ICA在进行特征抽取过程中的高维小样本问题;将局部人脸图像作为训练样本,这不仅增加了训练样本数,而且有利于提取人脸局部特征;依次从训练样本的水平方向、垂直方向提取训练样本特征,使得提取的特征不仅维数更小,而且能更有效地反映样本的局部信息.以上优点使得提出的算法较传统方法在人脸识别方面更稳定,识别率更高,在Yale人脸库和AR人脸库上验证了该算法的有效性.