Description: 为了实现实时的低带宽和基于手机平台的情感通信，传输的数据量必须足够小，鉴于人脸视频图像的表情信息和纹理信息量很大，我们采用了基于特征点的人脸表情再现和视频传输。因此特征点序列的预处理对人脸表情卡通话显得尤为重要.-In order to realize low-bandwidth and real-time mobile phone platform based on emotional communication, the amount of data transmission must be small enough, in view of facial expression video image information and a great texture information, we have adopted a feature points based on the Facial Expression and video reproduction transmission. Therefore feature point sequences preconditioning on Facial Expression, then a cartoon seems particularly important. Platform: |
Size: 4384768 |
Author:fjwang |
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Description: 一个PPT：基于肤色和AAM的人脸特征点定位及其应用-A PPT: color and AAM-based Facial Feature Point Positioning and Its Applications Platform: |
Size: 5083136 |
Author:张三 |
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Description: 本书对目前常用的人脸特征点定位算法和模型进行了总结，并在前人的基础
上提出一个基于肤色模型的对AAM 模型的改进方法-This book is currently used for facial feature point localization algorithm and model are summarized, and submitted on the basis of our predecessors of a model based on the color model of the AAM method Platform: |
Size: 2007040 |
Author:李尊 |
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Description: 摘 要： 提出了一种基于改进 Ha u s d o r f f距离的人脸相似度匹配的方法， 该方法首先将人脸划分为脸型、 双眼、 鼻、 嘴等几个特征点
集， 分别计算各部分的改进 Ha u s d o r f f 距离， 然后进行加权计算相似度。利用该方法， 在 A S M( 主动形状模型) 定位人脸的基础上进
行了人脸检索。 实验表明， 利用人脸相似度计算方法对人脸特征库进行搜索， 达到 了较好的效果。同时结合 A S M 自动人脸检测， 本
方法可以全自动完成人脸匹配， 应用于人脸识别及数字娱乐等领域。-Abstract: This paper presents a distance based on an improved Ha usdorff face similarity matching method, which will first face is divided into face, eyes, nose, mouth, and several other feature point sets, were calculated to improve the various parts of Ha usdorff distance, and then weighted similarity. Using this method, in the ASM (active shape model) positioned on the basis of human face human face retrieval. Experiments show that the use of face similarity calculation method for facial feature database to search, to achieve good results. ASM automatically combined with face detection, this method can be completed fully automatic human face matching, used in face recognition and digital entertainment and other fields. Platform: |
Size: 202752 |
Author:alsocc |
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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: A very good paper that explain tracking facial feature under varying face pose and facial expression-This paper presents a hierarchical multi-state pose-dependent approach for facial feature detection and tracking under varying facial expression and face pose. For effective and efficient representation of feature points, a hybrid representation that integrates Gabor wavelets and gray-level profiles is proposed. To model the spatial relations among feature points, a hierarchical statistical face shape model is proposed to characterize both the global shape of human face and the local structural details of each facial component. Furthermore, multi-state local shape models are introduced to deal with shape variations of some facial components under different facial expressions. During detection and tracking, both facial component states and feature point positions, constrained by the hierarchical face shape model, are dynamically estimated using a switching hypothesized measurements (SHM) model. Experimental results demonstrate that the proposed method accurately and robustly tracks faci Platform: |
Size: 958464 |
Author:Ng Jack |
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Description: We propose an algorithm for facial expression recognition which can classify the given image into one of the seven basic facial expression categories (happiness, sadness, fear, surprise, anger, disgust and neutral). PCA is used for dimensionality reduction in input data while retaining those characteristics of the data set that contribute most to its variance, by keeping lower-order principal components and ignoring higher-order ones. Such low-order components contain the "most important" aspects of the data. The extracted feature vectors in the reduced space are used to train the supervised Neural Network classifier. This approach results extremely powerful because it does not require the detection of any reference point or node grid. The proposed method is fast and can be used for real-time applications. Platform: |
Size: 21504 |
Author:mhm |
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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: The face can communicate varied personal information
including subjective emotion, communicative intent, and
cognitive appraisal. Accurate interpretation by observer or
computer interface depends on attention to dynamic properties
of the expression, context, and knowledge of what
is normative for a given individual. In two separate studies,
we investigated individual differences in the base rate
of positive facial expression and in specific facial action
units over intervals from 4- to 12 months. Facial expression
was measured using convergent measures, including facial
EMG, automatic feature-point tracking, and manual FACS
coding. Platform: |
Size: 61440 |
Author:payal |
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Description: 提出一种人脸特征点跟踪方法。首先, 运用ASM 获取人脸的特征点, 然后在选用Lucas Kanade光流算法的基础
上, 将初始帧和当前帧的前一帧有机结合起来, 共同作为当前帧的参考来进行特征点跟踪。实验结果表明该方法对特征
点的跟踪有很好的效果。-Proposed a facial feature point tracking method. First, the use of the ASM obtaining human face feature point, and then in the selection of the the Lucas Kanade optical flow algorithm, based on, the initial frame and the current frame before a combine, as the current frame of reference common feature point tracking . Experimental results show that the method has a good effect on the tracking of the feature point. Platform: |
Size: 231424 |
Author:yaomeng |
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Description: 研究优化人脸特征提取问题，针对长期以来在不贴标记点的情况下用传统的光流、Snake、可变模板等方法对纹理特征变化大的特征点不能有效跟踪，并且解决单独采用Gabor 小波系统开销大等问题，为了在人脸图像中提取准确信息，提出了人脸特征点的跟踪方法，分组采用改进的光流法和弹性图匹配的方法进行特征点跟踪。对眼睛、眉毛、上下眼皮等14 个表
情变化不大的特征点使用光流法进行跟踪，最后对变化大的嘴部8 个特征点运用Gabor 小波的弹性图匹配方法进行仿真。-Gabor wavelet research to optimize facial feature extraction problem for a long time in the case of stickers marked point changes of texture features with traditional optical flow, Snake, variable template feature points can not effectively track and solve alone overhead and large, in order to extract the accurate information in a face image, the facial feature point tracking method, grouping, improved optical flow method and elastic graph matching feature point tracking. Little change in the feature point of eyes, eyebrows, and eyelids 14 expression using the optical flow method for tracking simulation using Gabor wavelet elastic graph matching method, and finally on the changes of eight characteristic points of the mouth portion. Platform: |
Size: 444416 |
Author:yaomeng |
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Description: 基于C#编写的人脸识别系统，首先进行人脸图片的预处理，包括：标记人脸区域，光照补偿，灰度化，高斯平滑，直方图均衡化处理，对比度增强，二值化变换，去除孤立点等操作，然后标记人脸特征点，提取特征信息，与数据库中信息比较进行识别-Written in C#-based face recognition system, the first face image preprocessing, including: labeled human face region, illumination compensation, graying, Gaussian smoothing, histogram equalization, contrast enhancement, binarization transformation to removeisolated point operation, and then mark the facial feature points extracted feature information compared with the information in the database to identify Platform: |
Size: 5063680 |
Author:王华伟 |
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Description: asm人脸特征点标定辅助工具，核心为利用ASM算法检测人脸特征点，可以通过鼠标和键盘来选择和移动调整特征点，鼠标点击选取特征点，a w s d控制特征点移动。-asm facial feature point calibration aids, the core for the use of ASM algorithm detects facial feature points, via mouse and keyboard to select and move the adjusted characteristic point, the mouse click select the feature points, awsd control feature point moves. Platform: |
Size: 140288 |
Author:朱丽丽 |
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Description: 脸部特征点提取技术研究及其在三维人脸建模中的应用，实ING现了人脸的特征提取，并形成了人脸的建模-Facial feature point extraction technology research and in 3D face modeling application, ING is now a real human face feature extraction, and formed a human face modeling Platform: |
Size: 3995648 |
Author:路粮户 |
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Description: 这个是面部表情自动识别的源代码。它是基于Viola和Jones的面部检测算法和Gabor面部特征点的提取，然后使用训练过的神经网络来识别人脸表情。-This is the automatic recognition of facial expression source code. It is Viola and Jones face detection algorithms and Gabor facial feature point extraction, and then use the trained neural network based on facial expression recognition. Platform: |
Size: 14336 |
Author:彭格格 |
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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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