共查询到17条相似文献,搜索用时 328 毫秒
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提出一种新的基于SVM RFE(Support Vector Machine Recursive Feature Elimination)的人脸特征选择方法。该方法将权重矢量和半径/间隔作为SVM RFE的特征选择标准,采用缩放因子梯度算法优化特征搜索。基于该方法构建了一种实用、有效的人脸特征提取、选择及识别框架,并在UMIST人脸数据库上进行了验证实验。对特征选择前后的分类能力及速度进行了分析比较,结果表明,该方法是一种实用、有效的人脸特征选择方法,可以在特征维数为80左右时,达到94.62%的分类识别率。 相似文献
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为了克服人脸识别中存在的遮挡等闭塞问题,本文提出了Gabor特征结合Metaface学习的扩展稀疏表示人脸识别算法(GMFL)。考虑到Gabor局部特征对光照、表情和姿态等变化的鲁棒性,该算法首先提取图像的Gabor特征集;然后对Gabor特征集进行Metaface字典学习得到具有更强稀疏表示能力的新字典,同时引入Gabor闭塞字典来编码表示图像中的闭塞部分,并与新字典联合构造一组过完备字典基;最后利用过完备字典基求解稀疏系数重构样本,根据样本与重构样本之间的残差最小原则对人脸图像进行分类识别。在AR人脸库和FERET数据库上的实验结果验证了本文算法的可行性和有效性。 相似文献
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基于Gabor滤波系数高阶矩的图像检索 总被引:1,自引:0,他引:1
在分析Gabor滤波器进行图像纹理特征提取的基础上,提出了利用多尺度和多方向Gabor滤波系数的高阶矩提取图像特征进行CBIR的方法,利用滤波系数的方差给出了基于Gabor滤波组提取的图像纹理特征的平滑度和纹理一致性算法,并采用四个尺度和六个方向的滤波系数的能量、方差、峰态、平滑度和一致性组成了CBIR特征向量.采用Brodatz纹理库和Corel图像库中的典型图像进行了对比实验.实验证明,提出的方法比传统的Gabor滤波进行CBIR具有更高的查准率. 相似文献
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基于混沌理论和支持向量机的人脸识别方法 总被引:2,自引:0,他引:2
针对如何选定主成分分析(PCA)特征维数和如何选定支持向量机(SVM)的参数来进一步提高人脸识别系统性能的问题,提出了一种基于混沌理论和支持向量机的人脸识别方法.首先,在统一的目标函数下,在采用PCA方法对人脸图像进行降维和将得到的特征送入SVM中进行训练期间,使用具有可操作性的改进混沌优化算法同时对PCA图像特征维数和分类器参数进行优化选择,然后用得到的优化人脸特征和最佳参数的分类器对未知图像进行识别.基于该方法,对ORL和Yale人脸库进行实验,其识别率都高达99%以上,仿真结果表明,该方法极大地提高了人脸识别能力. 相似文献
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提出了一种基于条件互信息的图像特征选择方法.为了预测条件互信息,该方法选择与已选定特征具有最大熵的那些特征,并将选择出的特征进一步用于数字图像识别.图像识别器由支持向量机实现.实验中,识别器的输人数据是由人脸和非人脸组成的二类图像,这些图像均为大小是28×28且具有256个值的灰度图像.本文不仅将新方法用于图像识别,而且还将新方法与已有的识别方法,如经典的贝叶斯理论、神经网络、kNN等进行了比较.实验结果表明:新方法不仅能够在较短的时间内实现图像特征的选择,而且对图像识别有着比其它方法更高的正确识别率,完全可以用于图像识别. 相似文献
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Machine analysis of facial emotion recognition is a challenging and an innovative research topic in human–computer interaction. Though a face displays different facial expressions, which can be immediately recognized by human eyes, it is very hard for a computer to extract and use the information content from these expressions. This paper proposes an approach for emotion recognition based on facial components. The local features are extracted in each frame using Gabor wavelets with selected scales and orientations. These features are passed on to an ensemble classifier for detecting the location of face region. From the signature of each pixel on the face, the eye and the mouth regions are detected using the ensemble classifier. The eye and the mouth features are extracted using normalized semi-local binary patterns. The multiclass Adaboost algorithm is used to select and classify these discriminative features for recognizing the emotion of the face. The developed methods are deployed on the RML, CK and CMU-MIT databases, and they exhibit significant performance improvement owing to their novel features when compared with the existing techniques. 相似文献
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Masoud Muhammed Hassan Haval Ismael Hussein Adel Sabry Eesa Ramadhan J. Mstafa 《计算机、材料和连续体(英文)》2021,68(2):1637-1659
Over the past few decades, face recognition has become the most effective biometric technique in recognizing people’s identity, as it is widely used in many areas of our daily lives. However, it is a challenging technique since facial images vary in rotations, expressions, and illuminations. To minimize the impact of these challenges, exploiting information from various feature extraction methods is recommended since one of the most critical tasks in face recognition system is the extraction of facial features. Therefore, this paper presents a new approach to face recognition based on the fusion of Gabor-based feature extraction, Fast Independent Component Analysis (FastICA), and Linear Discriminant Analysis (LDA). In the presented method, first, face images are transformed to grayscale and resized to have a uniform size. After that, facial features are extracted from the aligned face image using Gabor, FastICA, and LDA methods. Finally, the nearest distance classifier is utilized to recognize the identity of the individuals. Here, the performance of six distance classifiers, namely Euclidean, Cosine, Bray-Curtis, Mahalanobis, Correlation, and Manhattan, are investigated. Experimental results revealed that the presented method attains a higher rank-one recognition rate compared to the recent approaches in the literature on four benchmarked face datasets: ORL, GT, FEI, and Yale. Moreover, it showed that the proposed method not only helps in better extracting the features but also in improving the overall efficiency of the facial recognition system. 相似文献
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Vehicle type recognition (VTR) is an important research topic due to its
significance in intelligent transportation systems. However, recognizing vehicle type on
the real-world images is challenging due to the illumination change, partial occlusion
under real traffic environment. These difficulties limit the performance of current stateof-art methods, which are typically based on single-stage classification without
considering feature availability. To address such difficulties, this paper proposes a twostage vehicle type recognition method combining the most effective Gabor features. The
first stage leverages edge features to classify vehicles by size into big or small via a
similarity k-nearest neighbor classifier (SKNNC). Further the more specific vehicle type
such as bus, truck, sedan or van is recognized by the second stage classification, which
leverages the most effective Gabor features extracted by a set of Gabor wavelet kernels
on the partitioned key patches via a kernel sparse representation-based classifier (KSRC).
A verification and correction step based on minimum residual analysis is proposed to
enhance the reliability of the VTR. To improve VTR efficiency, the most effective Gabor
features are selected through gray relational analysis that leverages the correlation
between Gabor feature image and the original image. Experimental results demonstrate
that the proposed method not only improves the accuracy of VTR but also enhances the
recognition robustness to illumination change and partial occlusion. 相似文献
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Hu‐Chuan Lu Ying‐Jie Huang Yen‐Wei Chen 《International journal of imaging systems and technology》2010,20(3):253-260
PCA, ICA, and Gabor wavelet are considered as the important and powerful face representation methods. In this article, we propose a new approach for face representation, which is called a pixel‐pattern‐based texture feature (PPBTF) and apply it to the real‐time facial expression recognition. A gray scale image is transformed into a pattern map where edges and lines are used for characterizing the facial texture information. Based on the pattern map, a feature vector is comprised of the numbers of the pixels belonging to each pattern. We use the image basis functions obtained by principal component analysis as the templates for pattern matching. Adaboost and Support Vector Machine are adopted to classify facial expression. Extensive experiments on the Cohn‐Kanade Database, PIE Database, and DUT Database illustrate that the PPBTF is quite effective and insensitive to illumination. The comparison with Gabor show the PPBTF is speedy. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 253–260, 2010 相似文献