Description: 便于使用的收集人脸图像建立数据库的工具程序。供人脸检测/识别/表情/姿态等模式识别、人工智能领域的研究者使用。可以方便地从网页中收集人脸照片，提供良好的交互界面对照片在模式识别，尤其是biometric领域中所需的各种属性进行标注，建立数据库。-easy-to-use collection of facial image database tools procedures. For Face detection / identification / expression / gestures, such as pattern recognition, artificial intelligence researchers in the field use. Can easily collected from the website pictures of faces with a good interface for photos in pattern recognition, particularly biometric field for the various attributes tagging, the establishment of databases. Platform: |
Size: 60838 |
Author:冯雪涛 |
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Description: 便于使用的收集人脸图像建立数据库的工具程序。供人脸检测/识别/表情/姿态等模式识别、人工智能领域的研究者使用。可以方便地从网页中收集人脸照片，提供良好的交互界面对照片在模式识别，尤其是biometric领域中所需的各种属性进行标注，建立数据库。-easy-to-use collection of facial image database tools procedures. For Face detection/identification/expression/gestures, such as pattern recognition, artificial intelligence researchers in the field use. Can easily collected from the website pictures of faces with a good interface for photos in pattern recognition, particularly biometric field for the various attributes tagging, the establishment of databases. Platform: |
Size: 60416 |
Author:冯雪涛 |
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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 Platform: |
Size: 1024 |
Author:bobo |
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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. Platform: |
Size: 10378240 |
Author:幺幺 |
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Description: Real-Time Facial Feature Point Extraction-Localization of facial feature points is an important step for many subsequent facial image analysis tasks. In this paper, we proposed a new coarse-to-fine method for extracting 20 facial feature points from image sequences. In particular, the Viola-Jones face detection method is extended to detect small-scale facial components with wide shape variations, and linear Kalman filters are used to smoothly track the feature points by handling detection errors and head rotations. The proposed method achieved higher than 90 detection rate when tested on the BioID face database and the FG-NET facial expression database. Moreover, our method shows robust performance against the variation of face resolutions and facial expressions. Platform: |
Size: 871424 |
Author:Ng Jack |
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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
Platform: |
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. Platform: |
Size: 375808 |
Author:MJ |
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Description: aleix@ecn.
Semantic queries to a database of images are more
desirable than low-level feature queries, because they
facilitate the user s task. One such approach is the
object-related image retrieval. In the context of face
images, it is of interest to retrieve images based on
people s names and facial expressions. However, when
images of the database are allowed to appear at dif-
ferent facial expressions, the face recognition approach
encounters the expression-invariant problem, i.e. how
to robustly identify a person s face for which its learn- Platform: |
Size: 152576 |
Author:a v |
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Description: aleix@ecn.
Semantic queries to a database of images are more
desirable than low-level feature queries, because they
facilitate the user s task. One such approach is the
object-related image retrieval. In the context
people s names and facial expressions. However, when
images of the database are allowed to appear at dif-
ferent facial expressions, the face recognition approach
encounters the expression-invariant problem, i.e. how
to robustly identify a person s face for which its learn- Platform: |
Size: 357376 |
Author:a v |
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Description:
facilitate the user s task. One such approach is the
object-related image retrieval. In the context of face
images, it is of interest to retrieve images based on
people s names and facial expressions. However, when
images of the database are allowed to appear at dif-
ferent facial expressions, the face recognition approach
encounters the expression-invariant problem, i.e. how
to robustly identify a person s face for which its learn- Platform: |
Size: 23552 |
Author:a v |
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Description: aleix@ecn.
Semantic queries to a database of images are more
desirable than low-level feature queries, because they
facilitate the user s task. One such approach is the
object-related image retrieval. In the context of face
images, it is of interest to retrieve images based on
people s names and facial expressions. However, when
images of the database are allowed to appear at dif-
ferent facial expressions, the face recognition approach
encounters the expression-invariant problem, i.e. how
to robustly identify a person s face for which its learn- Platform: |
Size: 47104 |
Author:a v |
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Description: aleix@ecn.
Semantic queries to a database of images are more
desirable than low-level feature queries, because they
facilitate the user s task. One such approach is the
object-related image retrieval. In the context of face
images, it is of interest to retrieve images based on
people s names and facial expressions. However, when
images of the database are allowed to appear at dif-
ferent facial expressions, the face recognition approach
encounters the expression-invariant problem, i.e. how
to robustly identify a person s face for which its learn- Platform: |
Size: 80896 |
Author:a v |
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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). Platform: |
Size: 10331136 |
Author:柯柯范儿 |
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Description: Frontal views of all subjects are videotaped under
constant illumination using fixed light sources, and none
of the subjects wear eyeglasses. These constraints are
imposed to minimize optical flow degradation. Previously
untrained subjects are video recorded performing a series
of expressions, and the image sequences are coded by
certified FACS coders. Facial expressions are analyzed in
digitized image sequences of arbitrary length (expression
sequences neutral to peak vary 9 to 44 frames).
60 subjects, both male and female, the larger
database were used in this study. The study includes more
than 260 image sequences and 5000 images. Subjects
ranged in age (18-35) and ethnicity (Caucasian, African-
American, and Asian/Indian).-Frontal views of all subjects are videotaped under
constant illumination using fixed light sources, and none
of the subjects wear eyeglasses. These constraints are
imposed to minimize optical flow degradation. Previously
untrained subjects are video recorded performing a series
of expressions, and the image sequences are coded by
certified FACS coders. Facial expressions are analyzed in
digitized image sequences of arbitrary length (expression
sequences neutral to peak vary 9 to 44 frames).
60 subjects, both male and female, the larger
database were used in this study. The study includes more
than 260 image sequences and 5000 images. Subjects
ranged in age (18-35) and ethnicity (Caucasian, African-
American, and Asian/Indian). Platform: |
Size: 1024 |
Author:qussai |
Hits:
Search - Facial Expression Image Database