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In order to recognize facial expression accurately, the paper proposed a hybrid method of principal component analysis (PCA) and local binary pattern (LBP). Firstly, the method of eight eyes segmentation was introduced to extract the effective area of facial expression image, which can reduce some useless information to subsequent feature extraction. Then PCA extracted the global grayscale feature of the whole facial expression image and reduced the data size at the same time. And LBP extracted local neighbor texture feature of the mouth area, which contributes most to facial expression recognition. Fusing the global and local feature will be more effective for facial expression recognition. Finally, support vector machine (SVM) used the fusion feature to complete facial expression recognition. Experiment results show that, the method proposed in this paper can classify different expressions more effectively and can get higher recognition rate than the traditional recognition methods. 相似文献
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人类面部表情是其心理情绪变化的最直观刻画,不同人的面部表情具有很大差异,现有表情识别方法均利用面部统计特征区分不同表情,其缺乏对于人脸细节信息的深度挖掘。根据心理学家对面部行为编码的定义可以看出,人脸的局部细节信息决定了其表情意义。因此该文提出一种基于多尺度细节增强的面部表情识别方法,针对面部表情受图像细节影响较大的特点,提出利用高斯金字塔提取图像细节信息,并对图像进行细节增强,从而强化人脸表情信息。针对面部表情的局部性特点,提出利用层次结构的局部梯度特征计算方法,描述面部特征点局部形状特征。最后,使用支持向量机(SVM)对面部表情进行分类。该文在CK+表情数据库中的实验结果表明,该方法不仅验证了图像细节对面部表情识别过程的重要作用,而且在小规模训练数据下也能够得到非常好的识别结果,表情平均识别率达到98.19%。 相似文献
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针对离散余弦变换(DCT)只能提取面部表情图像的全局特征,而忽略了临近像素之间的关系、不能提取纹理特征信息、不能准确区分相似表情等问题,提出一种融合离散余弦变换方法和局部二值模式(LBP)特征的表情特征提取方法。该方法首先将人脸图像经过DCT获得的低频系数作为表情的全局特征;然后用LBP对贡献率较大的嘴部、眼睛区域进行局部纹理特征提取,通过将LBP提取到的局部纹理特征与DCT提取到的全局特征进行融合,从而得到更有效的表情特征;最后利用支持向量机(SVM)进行识别。实验结果表明:该方法比单独使用DCT方法提取的表情特征更有利于识别,提高了表情识别的准确性,并将这个表情识别方法用于智能轮椅的控制上,收到了良好的效果。 相似文献
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针对局部二值模式(LBP)、中心对称局部二值模式(CS-LBP)和梯度方向直方图(HOG)的不足进行改进,该文提出中心对称局部平滑二值模式(CS-LSBP)和绝对梯度方向直方图(HOAG),并提出一种融合局部纹理特征和局部形状特征的人脸表情识别方法。该方法首先采用CS-LSBP算子和HOAG算子分别提取人脸表情图像的局部纹理特征和局部形状特征,然后使用典型线性分析法(CCA)进行特征融合,最后利用支持向量机(SVM)进行表情分类。在JAFFE人脸表情库和Cohn-Kanade(CK)人脸表情库上的实验结果表明,改进的特征提取方法能更加完整、精确地提取图像的细节信息,基于CCA的特征融合方法能充分发挥特征的表征能力,该文所提人脸表情识别方法取得了较好的分类识别效果。 相似文献
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This paper introduces a new face coding and recognition method, the enhanced Fisher classifier (EFC), which employs the enhanced Fisher linear discriminant model (EFM) on integrated shape and texture features. Shape encodes the feature geometry of a face while texture provides a normalized shape-free image. The dimensionalities of the shape and the texture spaces are first reduced using principal component analysis, constrained by the EFM for enhanced generalization. The corresponding reduced shape and texture features are then combined through a normalization procedure to form the integrated features that are processed by the EFM for face recognition. Experimental results, using 600 face images corresponding to 200 subjects of varying illumination and facial expressions, show that (1) the integrated shape and texture features carry the most discriminating information followed in order by textures, masked images, and shape images, and (2) the new coding and face recognition method, EFC, performs the best among the eigenfaces method using L(1) or L(2) distance measure, and the Mahalanobis distance classifiers using a common covariance matrix for all classes or a pooled within-class covariance matrix. In particular, EFC achieves 98.5% recognition accuracy using only 25 features. 相似文献
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针对表情识别,为提取对个体差异鲁棒性更强的特征,并有效利用特征自身分布特性,本文提出基于几何和纹理特征的表情层级分类方法.首先,构建基于中性脸相似度的几何特征提取方法,自动匹配样本相似中性脸,提取特征点比例系数几何特征;然后,利用充分矢量三角形提取纹理特征;最后,给出表情层级分类框架,在三个层级下分别利用提取特征判定表情类别.所提方法在JAFFE库和CK库上的实验结果表明,本文方法取得了比基于一般几何和纹理特征的识别方法更好的效果,证明了本文方法的有效性. 相似文献
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提出一种基于稀疏特征挑选(Sparse selection)和概率线性判别分析(Probabilistic linear discriminant analysis)的表情识别方法SS-PLDA.该方法由两部分构成:第一部分是使用稀疏的方法挑选出人脸与表情相关的区域,构造表情的完备特征集;第二部分是针对构造的表情完备特征集里仍含有一些其他信息,运用概率线性判别分析实现表情特征与干扰信息的分离,学习出一个只含有表情信息的子空间,最后基于该表情子空间进行表情识别分析.通过在CK+和JAFFE这两个数据库上面的实验,证实了基于稀疏特征挑选的方法可以得到识别性能的改善,且先使用特征挑选再对所挑选结果应用概率线性判别分析可以达到更好的提升效果. 相似文献
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Facial expressions contain most of the information on human face which is essential for human–computer interaction. Development of robust algorithms for automatic recognition of facial expressions with high recognition rates has been a challenge for the last 10 years. In this paper, we propose a novel feature selection procedure which recognizes basic facial expressions with high recognition rates by utilizing three-Dimensional (3D) geometrical facial feature positions. The paper presents a system of classifying expressions in one of the six basic emotional categories which are anger, disgust, fear, happiness, sadness, and surprise. The paper contributes on feature selections for each expression independently and achieves high recognition rates with the proposed geometric facial features selected for each expression. The novel feature selection procedure is entropy based, and it is employed independently for each of the six basic expressions. The system’s performance is evaluated using the 3D facial expression database, BU-3DFE. Experimental results show that the proposed method outperforms the latest methods reported in the literature. 相似文献
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Color local texture features for color face recognition 总被引:1,自引:0,他引:1
This paper proposes new color local texture features, i.e., color local Gabor wavelets (CLGWs) and color local binary pattern (CLBP), for the purpose of face recognition (FR). The proposed color local texture features are able to exploit the discriminative information derived from spatiochromatic texture patterns of different spectral channels within a certain local face region. Furthermore, in order to maximize a complementary effect taken by using both color and texture information, the opponent color texture features that capture the texture patterns of spatial interactions between spectral channels are also incorporated into the generation of CLGW and CLBP. In addition, to perform the final classification, multiple color local texture features (each corresponding to the associated color band) are combined within a feature-level fusion framework. Extensive and comparative experiments have been conducted to evaluate our color local texture features for FR on five public face databases, i.e., CMU-PIE, Color FERET, XM2VTSDB, SCface, and FRGC 2.0. Experimental results show that FR approaches using color local texture features impressively yield better recognition rates than FR approaches using only color or texture information. Particularly, compared with grayscale texture features, the proposed color local texture features are able to provide excellent recognition rates for face images taken under severe variation in illumination, as well as for small- (low-) resolution face images. In addition, the feasibility of our color local texture features has been successfully demonstrated by making comparisons with other state-of-the-art color FR methods. 相似文献
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针对局部三值模式(Local Ternary Pattern,LTP)对纹理特征描述不足的问题,提出一种基于局部幅度三值模式(Local Magnitude Ternary Pattern,LMTP)的人脸识别算法。首先将人脸图像进行分块处理。然后用LTP算子提取直方图以描述局部纹理的结构和LMTP算子提取直方图以描述局部纹理中像素值间的偏离幅度。最后将不同的直方图串联成LTP/LMTP直方图,将其作为人脸特征用于人脸匹配。实验分析表明,算法对纹理有更好的描述能力和在人脸识别中有更高的识别率,并对噪声有较好的鲁棒性。 相似文献
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针对视频表情识别,静态特征不能有效描述人脸区域沿时间轴动态变化信息的局限,该文提出一种融合动态纹理信息和运动信息的表情识别方法,借鉴LBP-TOP原理,提出具有时空域描述能力的时空韦伯局部描述子(STWLD)来提取动态纹理信息,同时采用分块光流直方图(BHOF)描述运动信息,最后利用SVM对融合后的纹理和运动信息完成表情分类。在CK+和MMI表情数据库上的交叉实验结果表明,相比基于单一特征的识别方法,所提方法取得了更好的效果;与其他相关方法的对比实验也验证了该方法的优越性。 相似文献
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在人脸表情识别中,针对Gabor小波变换特征维数很大的问题,提出了一种新的多方向特征编码方法。通过对Gabor特征幅值进行统计处理,将每个像素点同一尺度不同方向的Gabor特征幅值闽值化成二进制,加强了Gabor小波对图像局部结构信息的表征。同时,结合了类似旋转不变LBP的方法对图像进行降维。为了进一步提高表情的正确识别率,采用一种局部区域融合的方法,最后在JAFFE表情库上进行测试,得到比较好的识别率,验证了所提方法的有效性。 相似文献
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In this paper, a novel topology preserving non-negative matrix factorization (TPNMF) method is proposed for face recognition. We derive the TPNMF model from original NMF algorithm by preserving local topology structure. The TPNMF is based on minimizing the constraint gradient distance in the high-dimensional space. Compared with L(2) distance, the gradient distance is able to reveal latent manifold structure of face patterns. By using TPNMF decomposition, the high-dimensional face space is transformed into a local topology preserving subspace for face recognition. In comparison with PCA, LDA, and original NMF, which search only the Euclidean structure of face space, the proposed TPNMF finds an embedding that preserves local topology information, such as edges and texture. Theoretical analysis and derivation given also validate the property of TPNMF. Experimental results on three different databases, containing more than 12,000 face images under varying in lighting, facial expression, and pose, show that the proposed TPNMF approach provides a better representation of face patterns and achieves higher recognition rates than NMF. 相似文献
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基于二维Fisher线性判别的人脸耳组合识别 总被引:1,自引:1,他引:0
针对人脸易受到年龄、表情等影响,提出了脸和耳相结合的组合识别方法。利用二维Fisher线性判别(2DFLD)方法分别进行了脸、耳图像层和特征层的组合识别。在北京科技大学人耳库和ORL人脸库上进行实验,结果表明,图像层组合和特征层组合的识别率分别为97.5%、95.0%,分别比人脸识别提高了12.5%和10.0%,比人耳识别提高了5.0%和2.5%;与同样应用于组合识别的主成分分析(PCA)、二维PCA(2DPCA)比较,也取得了较好识别效果。这说明,多生物特征组合识别是一种有效的识别方法。 相似文献