Description: 利用快速傅立叶变换实现了人脸表情图像的Gabor变换的快速算法
针对不同尺度的Gabor小波特征采用不同的下采样因子来对Gabor变换特征进行第一次降维
利用一种改进的核鉴别分析方法对Gabor特征进行二次特征提取-using fast Fourier transform of Facial Expression Gabor transform the image of the fast algorithm for different foot the Gabor wavelet characteristics under different sampling factor to the Gabor transform features the first drop Shalikashvili Improved use of nuclear discriminant analysis method Gabor features second feature extraction Platform: |
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Author:zouchanjie |
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Description: 对由光源颜色变化引起的图像色彩偏差,进行了校正,并在YCbCr颜色空间建立了Cb-Cr色度查找表和亮度信息联合的肤色模型,应用预处理技术,去除部分非人脸区域,减少人脸检测的搜索空间,并采用模板匹配方法在人脸候选区域检测人脸.实验表明,该方法能够有效的从复杂环境的彩色图像中检测出左右旋转不超过45°的人脸,且不受人脸表情、尺度和数目的影响,且错误率较低.-Color by the light source caused by the change of image color deviation, a correction, and YCbCr color space established a Cb-Cr chrominance and luminance information look-up table of the color model of the joint application of pre-treatment technology, to remove some non-human face region, Face detection to reduce the search space, using a template matching method in the face candidate region detection of human faces. experiments show that the method can be effective in complex environments from color images detected no more than about 45 ° rotation of the human face, and from Facial Expression, scale and number of impact, and lower error rate. Platform: |
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Author:lll |
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Description: 这是完整的一套人脸表情识别,从样本训练到目标的识别.采用PCA来提取特征-This is the complete set of Facial Expression Recognition from the sample to the target identification training. Using PCA to extract the characteristics of Platform: |
Size: 261120 |
Author:史金玉 |
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Description: 人脸表情识别的Gabor小波变换，最终得到5个尺度，8个方向的不同特征，此程序参考文件Gabor feature classification using enhanced FLD model facial recognition-Facial Expression Recognition of Gabor wavelet transform, finally be five scale, eight directions of different characteristics, this procedure reference Gabor feature classification using enhanced FLD model facial recognition Platform: |
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Author:lily |
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Description: 1. 使用matlab自带的人脸识别工具（Viola-Jones算法）找出人脸的位置，并裁剪出人脸区域。
2. 使用Gabor滤波器识别出人脸的局部特征及纹理。
3. 训练一个SVM进行表情分类。
4. 交叉验证得到表情分类正确率为83.3 。
操作说明和系统描述请见ReadMe.-1. Using matlab with face detection tool (Viola-Jones algorithm) to find the location of a human face, and cut out the face region.
2. Use Gabor filter to identify the face local feature and texture.
3. Adopt SVM to train a facial expression classifier.
4. Use cross-validation to exam the classification accuracy: 83.3 . Platform: |
Size: 5593088 |
Author:冯溢 |
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Description: Matlab表情识别，特征脸[1 ]作为面部表情分类的方法。首先，利用训练图像创建低维人脸空间（pca）。这是通过训练图像集主成分分析（PCA）及图片主成分分析（即具有较大特征值的特征向量）获得的。
结果，所有的测试图像以所选择的主成分表示，计算投影图像与所有投影列车图像的欧几里得距离，选择最小值以找出与试验图像最相似的训练图像。(The feature face [1] is used as a facial expression classification method. Firstly, a low-dimensional face space (pca) is created using training images. This is obtained by training principal component analysis (PCA) of image set and principal component analysis of image (i.e. eigenvectors with larger eigenvalues).
As a result, all the test images are represented by the selected principal components, the Euclidean distance between the projected image and all the projected train images is calculated, and the minimum value is selected to find the training image most similar to the test image.) Platform: |
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Author:bbqQq |
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