Description: 该软件最主要的功能就是要能识别出人脸，首先该系统需要对通过摄像头拍照而获取到的原始的人脸图片进行一系列处理才可进行下一步的工作，该处理过程也称图像预处理。预处理这个模块在整个人脸识别系统的开发过程中占有很重要的地位，只有预处理模块做的好，才可能很好的完成后面的人脸定位和特征提取这两大关键模块。-The main features of the software is to be able to identify the face, first of all, the system need to take pictures through the camera and access to the original face image can only be carried out a series dealing with the work of the next step, the process also known as image pretreatment. Pretreatment of the Face Recognition System module in the entire process of development plays an important role, and only do pre-processing module, and can be very good after the completion of the Human Face Localization and Feature Extraction of these two key modules. Platform: |
Size: 1586176 |
Author:chiaks |
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Description: 实现一种结合颜色空间、变换及变形模板的自动唇部定位及唇轮廓提取、跟踪方法首先在空间建立肤色模型进行人脸检测、定位, 并由人脸几何特征进行唇部粗定位然后结合唇色模型进行变换使肤、唇色差别明显化, 提出根据亮度信息对变换结果预处理后用法进行图像分割, 经唇色模型进一步验证后实现唇部精定位再使用变形模板来进行嘴唇轮廓特征提取, 为增强内轮廓定位的鲁棒性, 对经亮度预处理和唇色模型验证得到的口腔区域边缘图进行曲线拟合来实现内轮廓定位最后, 将唇读图像序列中上一帧的唇部定位结果拓展后作为当前帧的预测区域再进行处理来实现唇动跟踪。-To achieve a combination of color space, transform and deformable template automatic lip localization and lip contour extraction, tracking the establishment of the first color model in the space of face detection, location, geometric features of the face by the rough location and then combined with lip lip transformation so that the skin color model, lip color difference visible, presented the results according to the luminance information to transform the image after pretreatment use of segmentation, and further validated by the lip model to achieve precise positioning and then use the deformation of the lip template for lip contour extraction , to enhance the robustness of location within the outline, by the brightness of the pretreatment and the lip model validation by oral Quyu edge map to fit curve to achieve the final positioning within the contour, the lip-reading image sequence in the last frame of the lips After positioning results extend the forecast area as the current frame Platform: |
Size: 970752 |
Author:郭事业 |
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Description: 人脸视频图像编码是近年来图像编码领域里的一个研究热点问题,它在通信、互联网等方面有着广泛的应用前景。人脸图像编码的研究包含很多子问题,主要的三个方面为:精确的人脸目标定位算法,实时的人脸目标跟踪算法和高效的人脸图像编码方法。本文的主要研究工作在于: 1) 提出帧间差分和背景差分相结合的人脸目标定位算法,在人脸特征选择上,主要使用形状特征,通过椭圆拟合来得到人脸的位置。 2) 采用基于Kalman滤波的运动预测方法对人脸的区域变化进行预测,以此缩小目标可能存在的区域(ROI).-Facial video image coding image compression in recent years a hot research topic in the field of the problem, it is in the communication, the Internet have a wide range of applications. Coding of face images contain many sub-problems, the main three are: accurate target location algorithm for face, real-time face tracking algorithm and efficient facial image coding method. This major research work are: 1) of the frame difference and background difference goal of combining face localization algorithm, feature selection in the human face, the main use of shape features, by ellipse fitting to get the location of the face. 2) Kalman filtering based motion prediction method changes the face of the region predicted to narrow the possible target area (ROI). Platform: |
Size: 1024 |
Author:周参 |
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Description: 基于人脸结构特征的眼睛定位算法论文，值得学习。-Based on structural features of the eyes, face localization algorithm paper, worth learning. Platform: |
Size: 397312 |
Author:mao |
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Description: 研究一种红外医学图像处理与分析方法,实现红外人脸图像中特征区域的自动定位。方法
针对红外正面脸部图像,采用一种无监督的局部和全局的特征提取方法,首先通过阈值法区分出前景和
背景,并根据面部特征对称性在前景中确定鼻区 然后在面部确定一个包含所有特征的矩形区域,利用
Harris算子在该区域检测出角点,并找出这些点的局部最大值点 最后用K-means方法对这些点进行
聚类
-To develop an mi age analyzing procedure forautomatic localization of facial fea-
tures on infrared mi ages.M ethodsAn unsupervised localand global features extractionmethodwas
adopted for the localization of facial features of frontalview face mi age. First, a thresholdwas used
to segment the mi age into foreground and background, and the nose was localized by considering
the symmetry of the face. Second, Harris operatorwas adopted to detect interest points in a rec-
tangulararea covering allthe facial features, and then localmaxmi um of the interestpointswere de-
tected. And finally, K-means clusteringmethodwas used to cluster the points and obtain the facial
features localization.
Platform: |
Size: 165888 |
Author:高雪 |
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Description: 该系统基于vc++开发环境具有目标跟踪检测测量车牌识别人脸定位火源检测等功能系统，还有一套仿生复眼全景目标跟踪系统-The system is based on vc++ development environment with the target tracking and detection measurement LPR face localization features such as fire detection systems, as well as a panoramic view of the target tracking system bionic compound eye Platform: |
Size: 6808576 |
Author:王东 |
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Description: 基于 AAM 的人脸特征定位方法在建立人脸模型过程中，不但考虑
局部特征信息，而且综合考虑到全局形状和纹理信息，通过对人脸形状特征和纹
理特征进行统计分析，建立人脸混合模型，即为最终对应的 AAM 模型。-AAM facial features localization method based on face model in establishing the process, not only consider local feature information, and considering the overall shape and texture information through facial shape and texture features for statistical analysis, facial hybrid model , is the final corresponding AAM model. Platform: |
Size: 711680 |
Author:李猛 |
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Description: 人脸检测方法主要有基于知识的方法、特征不变方法、模板匹配方法、基于外观的方法；脸部特征定位方法分为基于先验知识、几何形状、色彩、外观和关联信息等五类；人脸表情识别方法分为基于几何特征的识别方法，基于整体的识别方法，基于模型的识别方法，基于模板的识别方法。-Face detection methods are mainly based on knowledge of the method, the characteristics of constant method, template matching method, the method based on appearance Facial features localization method based on prior knowledge, geometric shape, color, appearance and associated information and so on five categories Facial expression recognition method, divided into recognition method based on the geometric features and recognition method based on the whole, the recognition method based on the model, the identification method based on template. Platform: |
Size: 1384448 |
Author:国俊迈 |
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Description: 人脸特征点定位，将深度学习运用到人脸对齐中，卷积神经网络
-Facial features localization, depth learning to use face alignment, the convolutional neural network Platform: |
Size: 43268096 |
Author:zhunianguo |
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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 features localization