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Description: 为了更准确地识别人的表情，在识别人脸7 种基本表情（愤怒、厌恶、恐惧、高兴、无表情、悲伤和惊讶）时，采用了局域二值
模式技术提取面部特征，进行由粗略到精细的表情分类。在粗略分类阶段，7 种基本表情中的2 种表情被选为初步分类结果（候选表情）。
在精细分类阶段，选用计算加权卡方值确定最终分类结果。采用日本的Jaffe 表情数据库来验证算法性能，对陌生人表情的识别率为77.9％，
其结果优于采用同样数据库的其他方法，且易于实现
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Author: 张波 |
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Description: 日本jaffe人脸表情数据库，包括216张人脸表情图像数据，是做表情朋友们必不可少的资料
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Author: longjie |
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Description: 为了更准确地识别人的表情，在识别人脸7 种基本表情（愤怒、厌恶、恐惧、高兴、无表情、悲伤和惊讶）时，采用了局域二值
模式技术提取面部特征，进行由粗略到精细的表情分类。在粗略分类阶段，7 种基本表情中的2 种表情被选为初步分类结果（候选表情）。
在精细分类阶段，选用计算加权卡方值确定最终分类结果。采用日本的Jaffe 表情数据库来验证算法性能，对陌生人表情的识别率为77.9％，
其结果优于采用同样数据库的其他方法，且易于实现-In order to more accurately identify the person s facial expressions, in the identification of seven kinds of basic facial expressions (anger, disgust, fear, happy, expressionless, sadness and surprise), the use of local binary pattern of facial features extraction techniques, carried out by the rough to the fine expression classification. In the rough classification stage, the seven kinds of basic expressions in the two kinds of expression was selected as the initial classification results (candidate expressions). In the fine classification stage, the choice of calculating the weighted chi-square value to determine the final classification results. Jaffe expressions used in Japan to validate algorithm performance database of strangers face recognition rate was 77.9, the result is better than using the same database in other ways, and are easy to achieve
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Size: 212992 |
Author: 张波 |
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Description:
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Size: 14349312 |
Author: longjie |
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Description: 提取人脸表情库中的图像数据,可提取任一种表情、任一个人或任一复本-extract the image data of facial expression database:JAFFE database。it is able to extract any expression, subject and copy of JAFFE database
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Size: 1024 |
Author: bobo |
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Description: The Japanese Female Facial Expression (JAFFE) Database
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Size: 10805248 |
Author: lam |
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Description: 摘要：在仔细分析证件照片中人脸特点的基础上，提出了一种把人脸的几何特征矢量匹配和人脸的分块加权匹配相结合的思想。该方法针
对一般人脸识别方法不能有效消除人脸表情影响的特点，首先对人脸进行快速准确的眼睛定位、图像摆正以及标准化处理，然后一方面抽取
能够避免人脸表情影响的几何特征向量，另一方面对标准人脸进行分块加权匹配，最后进行综合识别。对JAFFE人脸库的试验结果表明，该方法识别准确率高，能够有效地消除人脸表情在识别中的影响，结果令人满意。-A Mixed Face Recognition Method Based on ID Card
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Author: 张力 |
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Description: 日本ATR（Advanced Telecommunication Research InstituteInternational）的专门用于表情识别研究的基本表情数据库JAFFE，该数据库中包含了213幅（每幅图像的分辨率：256像素×256像素）日本女性的脸相，每幅图像都有原始的表情定义。表情库中共有10个人，每个人有7种表情（中性脸、高兴、悲伤、惊奇、愤怒、厌恶、恐惧）。 JAFFE数据库均为正面脸相，且把原始图像进行重新调整和修剪，使得眼睛在数据库图像中的位置大致相同，脸部尺寸基本一致，光照均为正面光源，但光照强度有差异。由于此表情数据库完全开放，且表情标定很标准，所以现在大多数研究表情识别的文章中都使用它来训练与测试。-Japan ATR (Advanced Telecommunication Research InstituteInternational), devoted to the basic expression of facial expression recognition research database JAFFE, the database contains 213 (each image resolution: 256 pixels × 256 pixels) Japanese women face relative to each piece of expression of both the original definition of the image. Expression library, a total of 10 individuals, each person has seven kinds of expressions (neutral face, happy, sad, surprise, anger, disgust, fear). JAFFE face database are positive phase, and the original image is re-adjust and trim, making the eye the location of the image in the database similar to the face basically the same size, light source are positive, but there are differences in light intensity. Since this expression database, completely open, and the expression of calibration is very standard, so now most of the research articles in both expression recognition use it to train and test.
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Author: 幺幺 |
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Description: DMMC算法在JAFFE 30*180训练与测试集下的人脸识别算法，其中包括源代码及所用的人脸库。-DMMC algorithm JAFFE 30* 180, under the training and test set of face recognition algorithms, including source code and libraries used by the human face.
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Author: lyhpjlcn |
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Description: 论文提出一种全自动识别人脸七种基本表情(愤怒、厌恶、恐惧、高兴、无表情、悲伤和惊奇)的方法。该方法首先
采用几何模型匹配法自动定位出人眼，在此基础上进行人脸大小归一化，然后利用局域二值模式(Local Binary Pattern．
LBP)技术提取面部特征，最后采用由粗到细的方案进行表情分类。采用日本的JAFFE公用表情数据库来检测算法的性
能，实验结果验证了方法的有效性。-Paper proposes a fully automatic identification of seven basic facial expressions (anger, disgust, fear, happy, neutral, sadness, and surprise) method. In this method, the geometric model matching method with automatic positioning to human eyes, in this based on the human face of Size Normalization and then use local binary pattern (Local Binary Pattern. LBP) Jishu extract facial features, and finally using coarse-to- fine program expression classification. Public expression of Japan' s JAFFE database performance detection algorithm, experimental results verify the validity of the method.
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Author: MJ |
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Description: 为了更有效地提取图像的局部特征，提出了一种基于2维偏最小二乘法(two—dimensional partial least
square，2DPLS)的图像局部特征提取方法，并将其应用于面部表情识别中。该方法首先利用局部二元模式(1ocal
binary pattern，LBP)算子提取一幅图像中所有子块的纹理特征，并将其组合成局部纹理特征矩阵。由于样本图像
被转化为局部纹理特征矩阵，因此可将传统PLS方法推广为2DPLS方法，用来提取其中的判别信息。2DPLS方法
通过对类成员关系矩阵的构造进行相应的修改，使其适应样本的矩阵形式，并能体现出人脸局部信息重要性的差
异。同时，对于类成员关系协方差矩阵的奇异性问题，也推导出了其广义逆的解析解。基于JAFFE人脸表情库的
实验结果表明，该方法不但可以有效地提取图像局部特征，并能取得良好的表情识别效果。-To better the image of the local feature extraction, a partial least squares method based on 2D (two-dimensional partial least
square, 2DPLS) image local feature extraction method, and applied to facial expression recognition. In this method, use of local binary pattern (1ocal
binary pattern, LBP) operator extracts an image texture features of all sub-blocks, and their combination into the local texture feature matrix. As the sample image
Be translated into the local texture feature matrix, so the traditional PLS method can be generalized to 2DPLS method used to extract the identification information. 2DPLS method
Through the class membership matrix in the corresponding modifications to adapt the sample matrix, and can reflect the importance of face poor local information
Different. Meanwhile, members of the class covariance matrix of the singular relations issues, also derived the generalized inverse of the analytical solution. Based on the JAFFE facial expression database
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Size: 315392 |
Author: MJ |
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Description: 提出了基于特征融合和模糊核判别分析(FKDA)的面部表情识别方法。首先，从每幅人脸图像中手工定
位34个基准点，作为面部表情图像的几何特征，同时采用Gabor小波变换方法对每幅表情图像进行变换，并提取基
准点处的Gabor小波系数值作为表情图像的Gabor特征；其次，利用典型相关分析技术对几何特征和Gabor特征进
行特征融合，作为表情识别的输人特征；然后，利用模糊核判别分析方法进一步提取表情的鉴别特征；最后，采用最
近邻分类器完成表情的分类识别。通过在JAFFE国际表情数据库和Ekman“面部表情图片”数据库上的实验，证实
了所提方法的有效性。-Proposed based on feature fusion and fuzzy kernel discriminant analysis (FKDA) facial expression recognition. First, face images of each piece of hand-set
Bit 34 basis points, as the geometric features of facial expression images, while using Gabor wavelet transform method to transform the images of each piece of expression, and extraction-based
Quasi-point of the Gabor wavelet coefficients, as Gabor features of facial expression image second, using canonical correlation analysis on the geometric features and Gabor features into
Line feature fusion, as expression recognition of input features then, using fuzzy kernel discriminant analysis method to extract and further identification features of expression Finally, the most
Neighbor classifier to complete expression of the classification. International expression by JAFFE database and Ekman "facial image" database on the experiment, confirmed
The proposed method.
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Size: 375808 |
Author: MJ |
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Description: 利用压缩感知算法实现人表情识别，在JAFFE数据库上实现的，利用Gabor特征-expression recognition based on compressed sensing in JAFFE database with Gabor
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Size: 5120 |
Author: 宋风 |
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Description: 数据库是由10个人的7种正面表情组成的213幅灰度图像，图像是以大小为256256的8位灰度级存储的，格式为.tiff型，平均每个人每种表情有2到4张。- the JAFFE database[11] which is
composed of 213 images of female facial expression
corresponding to 10 distinct subjects. Each image is stored at a
resolution of 256×256 pixels and 8-bit gray level. Each subject
in the database is represented with 7 categories of expression
(angry, disgust, fear, neutral, sadness, happiness and surprise).
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Author: 柯柯范儿 |
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Description: 人脸表情数据库，共有7类表情，每类8张，适合做人脸表情识别用-Facial expression database, a total of 7 class expression, 8 per class, suitable for a man with facial expression recognition
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Author: yuchen |
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Description: this paper gives comparison study of two feature extractor i.e gabor filter and log gabor filter. algorithms are applied to AR database and JAFFE database. the result found was that log gabor filter shows little better performance than gabor filter.
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Author: nanhi |
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Description: This project describes the problem of facial expression recognition in the field of computer vision. Firstly, the psychological background of the problem is presented. Then, the idea of facial expression recognition system (FERS) is outlined and the requirements are specified. The FER system consists of 3 stages: face detection, feature extraction and expression recognition. Methods proposed in literature are reviewed for each stage of a system. Finally, the design and implementation of this system are explained. The face detection algorithm used in the system is based on Viola-Jones. The features are obtained using Gabor features. The MultiSupport Vector Machine is used for classification. The used for facial expression system is JAFFE Database-This project describes the problem of facial expression recognition in the field of computer vision. Firstly, the psychological background of the problem is presented. Then, the idea of facial expression recognition system (FERS) is outlined and the requirements are specified. The FER system consists of 3 stages: face detection, feature extraction and expression recognition. Methods proposed in literature are reviewed for each stage of a system. Finally, the design and implementation of this system are explained. The face detection algorithm used in the system is based on Viola-Jones. The features are obtained using Gabor features. The MultiSupport Vector Machine is used for classification. The used for facial expression system is JAFFE Database
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Author: Jashpreet |
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Description: This project describes the problem of facial expression recognition in the field of computer vision. Firstly, the psychological background of the problem is presented. Then, the idea of facial expression recognition system (FERS) is outlined and the requirements are specified. The FER system consists of 3 stages: face detection, feature extraction and expression recognition. Methods proposed in literature are reviewed for each stage of a system. Finally, the design and implementation of this system are explained. The face detection algorithm used in the system is based on Viola-Jones. The features are obtained using Gabor features. The MultiSupport Vector Machine is used for classification. The used for facial expression system is JAFFE Database-This project describes the problem of facial expression recognition in the field of computer vision. Firstly, the psychological background of the problem is presented. Then, the idea of facial expression recognition system (FERS) is outlined and the requirements are specified. The FER system consists of 3 stages: face detection, feature extraction and expression recognition. Methods proposed in literature are reviewed for each stage of a system. Finally, the design and implementation of this system are explained. The face detection algorithm used in the system is based on Viola-Jones. The features are obtained using Gabor features. The MultiSupport Vector Machine is used for classification. The used for facial expression system is JAFFE Database
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Author: Jashpreet |
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Description: JAFFE人脸数据库文件，适合人脸识别-JAFFE face files, suitable for human face recognition......
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Author: 曹旭强 |
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Description: 通过训练jaffe数据库，实现识别人脸高兴、惊讶、恐惧、生气等六种表情，并圈出。可调用电脑摄像头实时监测。内附有使用说明，可以使用。仅供学习参考。(Through training Jaffe database, six kinds of facial expressions, such as happiness, surprise, fear and anger, are recognized and circled. Real-time monitoring with a computer camera can be invoked. With instructions for use, it can be used. For learning reference only.)
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Author: 我就是玩一玩 |
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