Description: The original source codes works on morphing two people faces as a whole and then combine them by cross dissolve. In this project, I have modified the code so that it works on one face and morph partially on the face, such as the mouth, left eye and right eye.-The original source codes works on morphin g two people faces as a whole and then combine the m by cross dissolve. In this project, I have modified the code so that it works on one fa ce and morph partially on the face, such as the mouth, left eye and right eye. Platform: |
Size: 19456 |
Author:sjtuzyk |
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Description: 用Matlab写的投影法提取嘴唇
输入图像必须是规定大小的人脸-Written using Matlab projection extraction lips input image must be provided for the size of face Platform: |
Size: 1024 |
Author:风之子 |
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Description: High information redundancy and correlation in face images result in efficiencies when such images are used directly for recognition. In this paper, discrete cosine transforms are used to reduce image information redundancy because only a subset of the transform coefficients are necessary to preserve the most important facial features such as hair outline, eyes and mouth. We demonstrate experimentally that when DCT coefficients are fed into a backpropagation neural network for classification, a high recognition rate can be achieved by using a very small proportion of transform coefficients. This makes DCT-based face recognition much faster than other approaches.-High information redundancy and correlation in face images result in inefficiencies when such images are used directly for recognition. In this paper, discrete cosine transforms are used to reduce image information redundancy because only a subset of the transform coefficients are necessary to preserve the most important facial features such as hair outline, eyes and mouth. We demonstrate experimentally that when DCT coefficients are fed into a backpropagation neural network for classification, a high recognition rate can be achieved by using a very small proportion of transform coefficients. This makes DCT-based face recognition much faster than other approaches. Platform: |
Size: 25600 |
Author:mhm |
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Description: Matlab平台下在人脸区域利用EyeMap和MoutMap算法检测眼睛-Matlab platform in the face region detection algorithm using EyeMap and MoutMap eyes Platform: |
Size: 1024 |
Author:jiangshui |
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Description: 主要是连通分量提取及实现,通过matlab实现的了在人脸局部图像中定位嘴的中心.-Mainly connected component extraction and realization, through matlab to achieve in the face of local images to locate the center of the mouth. Platform: |
Size: 1024 |
Author:tianwenxu |
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Description: 在人脸局部图像中定位嘴的中心。MATLAB环境代码，配有实验用图。具体步骤：对输入图像进行二值化处理-标注二值图像中的连通分量-找出最大的连通分量-计算最大联通分量中心。《精通Matlab数字图像处理与识别》-The face part image is positioned in the center of the mouth. MATLAB environment code, with experimental Fig. Specific steps: input image binarization processing- marked binary images connected components- find the largest connected component- component to calculate the maximum Unicom center. " Mastering Matlab digital image processing and recognition." Platform: |
Size: 3072 |
Author:廖欣 |
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Description: Lets see how to detect face, nose, mouth and eyes using the MATLAB built-in class and function. Based on Viola-Jones face detection algorithm, the computer vision system toolbox contains vision.CascadeObjectDetector System object which detects objects based on above mentioned algorithm. Platform: |
Size: 225280 |
Author:sirisha |
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Description: 人脸对齐基于论文《fps3000》中训练集构建时生产68个特征点的matlab脚本文件。提供交互窗口，按顺序点左眼右眼和嘴巴，即可自动生成68个点，再用鼠标对个别点进行人工修正。满意后保存即生成一个文本文件，存有68个点的坐标。-Face aligned on the paper fps3000 production when the training set to build 68 feature points matlab script file. Provide interactive window, press the right and the left dot sequence mouth, you can automatically generate 68 points, then the mouse on the individual points manually corrected. The satisfaction of saving a text file that is generated, there are the coordinates of 68 points. Platform: |
Size: 712704 |
Author:黄圣凯 |
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Description: ASM算法 MATLAB源代码
人脸特征点定位的目的是在人脸检测的基础上，进一步确定脸部特征点（眼睛、眉毛、鼻子、嘴巴、脸部外轮廓）的位置。定位算法的基本思路是：人脸的纹理特征和各个特征点之间的位置约束结合。-The purpose of the facial feature point positioning is to further determine the position of the facial feature points (eye, eyebrows, nose, mouth, contour of the face) on the basis of face detection. The basic idea of the localization algorithm is that the texture features of the face and the position constraint between the individual feature points are combined. The classical algorithm is ASM and AAM. Platform: |
Size: 152576 |
Author:曾 |
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Search - face mouth matlab