Matrix principal component analysis （MatPCA）, as an effective feature extraction method, can deal with the matrix pattern and the vector pattern. However, like PCA, MatPCA does not use the class information of sampl...Matrix principal component analysis （MatPCA）, as an effective feature extraction method, can deal with the matrix pattern and the vector pattern. However, like PCA, MatPCA does not use the class information of samples. As a result, the extracted features cannot provide enough useful information for distinguishing pat- tern from one another, and further resulting in degradation of classification performance. To fullly use class in- formation of samples, a novel method, called the fuzzy within-class MatPCA （F-WMatPCA）is proposed. F-WMatPCA utilizes the fuzzy K-nearest neighbor method（FKNN） to fuzzify the class membership degrees of a training sample and then performs fuzzy MatPCA within these patterns having the same class label. Due to more class information is used in feature extraction, F-WMatPCA can intuitively improve the classification perfor- mance. Experimental results in face databases and some benchmark datasets show that F-WMatPCA is effective and competitive than MatPCA. The experimental analysis on face image databases indicates that F-WMatPCA im- proves the recognition accuracy and is more stable and robust in performing classification than the existing method of fuzzy-based F-Fisherfaces.展开更多
Face recognition(FR) is a practical application of pattern recognition(PR) and remains a compelling topic in the study of computer vision. However, in real-world FR systems, interferences in images, including illumina...Face recognition(FR) is a practical application of pattern recognition(PR) and remains a compelling topic in the study of computer vision. However, in real-world FR systems, interferences in images, including illumination condition, occlusion, facial expression and pose variation, make the recognition task challenging. This study explored the impact of those interferences on FR performance and attempted to alleviate it by taking face symmetry into account. A novel and robust FR method was proposed by combining multi-mirror symmetry with local binary pattern(LBP), namely multi-mirror local binary pattern(MMLBP). To enhance FR performance with various interferences, the MMLBP can 1) adaptively compensate lighting under heterogeneous lighting conditions, and 2) generate extracted image features that are much closer to those under well-controlled conditions(i.e., frontal facial images without expression). Therefore, in contrast with the later variations of LBP, the symmetrical singular value decomposition representation(SSVDR) algorithm utilizing the facial symmetry and a state-of-art non-LBP method, the MMLBP method is shown to successfully handle various image interferences that are common in FR applications without preprocessing operation and a large number of training images. The proposed method was validated with four public data sets. According to our analysis, the MMLBP method was demonstrated to achieve robust performance regardless of image interferences.展开更多
A face recognition scheme is proposed, wherein a face image is preprocessed by pixel averaging and energy normalizing to reduce data dimension and brightness variation effect, followed by the Fourier transform to esti...A face recognition scheme is proposed, wherein a face image is preprocessed by pixel averaging and energy normalizing to reduce data dimension and brightness variation effect, followed by the Fourier transform to estimate the spectrum of the preprocessed image. The principal component analysis is conducted on the spectra of a face image to obtain eigen features. Combining eigen features with a Parzen classifier, experiments are taken on the ORL face database.展开更多
三维人脸数据的获取会受到成本以及可访问性的影响。通过对深度相机(如Xtion pro live)获取人脸数据过程的研究可知,它能够很容易获得彩色和深度结合(RGB-D)图。针对RGB-D图,使用局部和整体混合识别,利用局部二值的平均信息熵模式(LBEP)...三维人脸数据的获取会受到成本以及可访问性的影响。通过对深度相机(如Xtion pro live)获取人脸数据过程的研究可知,它能够很容易获得彩色和深度结合(RGB-D)图。针对RGB-D图,使用局部和整体混合识别,利用局部二值的平均信息熵模式(LBEP),快速提取RGB-D图的直方图信息和特征向量,根据不同区域在表情不同情况下的变化程度,对不同区域的识别效果赋予不同的权值,进行加权运算。实验结果表明,相比现有的二维和三维人脸识别算法,改进的LBEP算法识别率有明显的提升。展开更多
文摘Matrix principal component analysis （MatPCA）, as an effective feature extraction method, can deal with the matrix pattern and the vector pattern. However, like PCA, MatPCA does not use the class information of samples. As a result, the extracted features cannot provide enough useful information for distinguishing pat- tern from one another, and further resulting in degradation of classification performance. To fullly use class in- formation of samples, a novel method, called the fuzzy within-class MatPCA （F-WMatPCA）is proposed. F-WMatPCA utilizes the fuzzy K-nearest neighbor method（FKNN） to fuzzify the class membership degrees of a training sample and then performs fuzzy MatPCA within these patterns having the same class label. Due to more class information is used in feature extraction, F-WMatPCA can intuitively improve the classification perfor- mance. Experimental results in face databases and some benchmark datasets show that F-WMatPCA is effective and competitive than MatPCA. The experimental analysis on face image databases indicates that F-WMatPCA im- proves the recognition accuracy and is more stable and robust in performing classification than the existing method of fuzzy-based F-Fisherfaces.
基金supported by National Natural Science Foundation of China (No. 51305392)Youth Funds of the State Key Laboratory of Fluid Power Transmission and Control (No. SKLoFP_QN_1501)+1 种基金Zhejiang Provincial Natural Science Foundation of China (Nos. LY17E050009 and LZ15E050001)the Fundamental Rsesearch Funds for the Central Universities (No. 2018QNA4008)
文摘Face recognition(FR) is a practical application of pattern recognition(PR) and remains a compelling topic in the study of computer vision. However, in real-world FR systems, interferences in images, including illumination condition, occlusion, facial expression and pose variation, make the recognition task challenging. This study explored the impact of those interferences on FR performance and attempted to alleviate it by taking face symmetry into account. A novel and robust FR method was proposed by combining multi-mirror symmetry with local binary pattern(LBP), namely multi-mirror local binary pattern(MMLBP). To enhance FR performance with various interferences, the MMLBP can 1) adaptively compensate lighting under heterogeneous lighting conditions, and 2) generate extracted image features that are much closer to those under well-controlled conditions(i.e., frontal facial images without expression). Therefore, in contrast with the later variations of LBP, the symmetrical singular value decomposition representation(SSVDR) algorithm utilizing the facial symmetry and a state-of-art non-LBP method, the MMLBP method is shown to successfully handle various image interferences that are common in FR applications without preprocessing operation and a large number of training images. The proposed method was validated with four public data sets. According to our analysis, the MMLBP method was demonstrated to achieve robust performance regardless of image interferences.
文摘A face recognition scheme is proposed, wherein a face image is preprocessed by pixel averaging and energy normalizing to reduce data dimension and brightness variation effect, followed by the Fourier transform to estimate the spectrum of the preprocessed image. The principal component analysis is conducted on the spectra of a face image to obtain eigen features. Combining eigen features with a Parzen classifier, experiments are taken on the ORL face database.
文摘三维人脸数据的获取会受到成本以及可访问性的影响。通过对深度相机(如Xtion pro live)获取人脸数据过程的研究可知,它能够很容易获得彩色和深度结合(RGB-D)图。针对RGB-D图,使用局部和整体混合识别,利用局部二值的平均信息熵模式(LBEP),快速提取RGB-D图的直方图信息和特征向量,根据不同区域在表情不同情况下的变化程度,对不同区域的识别效果赋予不同的权值,进行加权运算。实验结果表明,相比现有的二维和三维人脸识别算法,改进的LBEP算法识别率有明显的提升。
