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: |
Size: 32768 |
Author:zouchanjie |
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Description: 为了更准确地识别人的表情，在识别人脸7 种基本表情（愤怒、厌恶、恐惧、高兴、无表情、悲伤和惊讶）时，采用了局域二值
模式技术提取面部特征，进行由粗略到精细的表情分类。在粗略分类阶段，7 种基本表情中的2 种表情被选为初步分类结果（候选表情）。
在精细分类阶段，选用计算加权卡方值确定最终分类结果。采用日本的Jaffe 表情数据库来验证算法性能，对陌生人表情的识别率为77.9％，
其结果优于采用同样数据库的其他方法，且易于实现-In order to more accurately identify the person s facial expressions, in the identification of seven kinds of basic facial expressions (anger, disgust, fear, happy, expressionless, sadness and surprise), the use of local binary pattern of facial features extraction techniques, carried out by the rough to the fine expression classification. In the rough classification stage, the seven kinds of basic expressions in the two kinds of expression was selected as the initial classification results (candidate expressions). In the fine classification stage, the choice of calculating the weighted chi-square value to determine the final classification results. Jaffe expressions used in Japan to validate algorithm performance database of strangers face recognition rate was 77.9, the result is better than using the same database in other ways, and are easy to achieve Platform: |
Size: 212992 |
Author:张波 |
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Description: 脸部特征提取 最近看到的 可以下载看看 挺好玩的-Facial feature extraction can be downloaded recently seen look very good playing Platform: |
Size: 28672 |
Author:张华振 |
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Description: 基于ORL人脸库的人脸识别的特征人脸的提取源代码。-Based on the ORL face database for face recognition facial features extraction source code. Platform: |
Size: 4096 |
Author:yuzhun21 |
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Description: 提出一种新的非线性变换的彩色空间 ″″, 利用次高斯概率分布函数拟合皮肤色度信息, 得到候选区
YC C
r b
域。为了排除候选区域中的非人脸, 首先根据均值和方差信息分割出候选区域中的纹理特征信息, 再通过多尺度
)
(
信息定位眼睛, 然后根据人脸特征的几
形态边缘检测算子检测候选区域的边缘, 利用 边缘方向
PCA PCAED
( )
何形状信息定位其他特征 鼻、嘴 , 通过这些几何特征信息对肤色分割得到的候选区域进行验证, 最终得到正确
的人脸区域。利用3 个实验数据集测试该算法, 并与其它相应的算法相比较, 提出的非线性彩色空间对于肤色分
割具有很好的效果, 且对光照和姿态具有良好的不变性。另外, 利用 信息和几何特征信息检测人脸特征
PCAED
具有很高的定位精度, 定位检测率优于其他方法。实验结果表明, 该算法具有定位准确率高, 漏检率和误检率低
等特点。-
A novel approach for skin segmentation and facial feature extraction is proposed
The proposed skin segmentation is a method for integrating the chrominance components of
″″ . ″″
r b r b
nonlinear YC C color model The chrominance components of nonlinear YC C color space
,
are modeled using a subgaussian probability density function and then the face skin is seg
. ,
mented based on this function In order to authenticate the face candidate regions firstly tex
ture information in face candidate regions is segmented using mean and variance of luminance
, . ,
information and then the eye is located by the PCA edge direction information Finally the
, ,
others features such as nose and mouth also are detected using the geometrical shape infor
. 2 ,
mation As all the above mentioned techniques are simple and efficient the skin segmentation
.
based on nonlinear color spacemethod has the invariability of lighting and pose In the experi
, .
ments themethod has been successfull Platform: |
Size: 458752 |
Author:zz |
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Description: 图象处理,边缘检测提取,定位特征区域对人面部进行识别-Image processing, edge detection extraction, Positioning human facial features of the region to identify Platform: |
Size: 244736 |
Author:qu |
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Description: ：针对传统Gabor变换在提取表情特征时，冗余较大、特征维数较高的不足，结合ASM
自动特征定位技术，提出了一种基于特征点Gabor特征和ASM 形状特征相融合的面部表情
识别方法．实验表明，两种特征的融合，可有效地利用特征点的局部纹理信息和脸部器官的整
体形状信息，达到了更好的面部表情识另4效果．-: Gabor transform traditional expression feature extraction, the redundancy large feature dimension is high enough, combined with ASM automatic feature location technique, the algorithm based on feature characteristics of Gabor features and the ASM shape fusion of facial expression recognition Methods. Experimental results show that the integration of two features, feature points can effectively use the local texture information and the overall shape of the face organs of information, to achieve a better effect of facial expression understanding the other 4. Platform: |
Size: 365568 |
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: 研究描述人脸特征的有效方法, 讨论身份证照片的特征提取和检索采用自适应肤色检测技术改进通用的肤色检测算法, 进行脸部区域的划分提出系数投影法对面部五官区域进行分割, 在各区域中提取面部几何特征引人描述脸颊和下额轮廓的曲线参数作为脸形特征, 得到对人脸特征更准确的描述将面部几何特征矢量匹配、脸形曲线参数匹配和脸部图像相关匹配相结合, 实现人像照片的准确检索实验表明该方法性能优良。-Describe the facial features of an effective way to discuss the ID card photo feature extraction and retrieval using adaptive skin color detection techniques improve the general detection algorithm for facial areas by the proposed coefficient of projection of the facial features to segment the region, in the Extraction of geometric features of the region face cheeks and under the places described in the introduction of profile parameters of the curve as facial features, facial features and get the more accurate description of the feature vector matching facial geometry, face curve parameter matching and matching facial images related to the combination of to achieve an accurate portrait photo retrieval experiments show that the method excellent. Platform: |
Size: 699392 |
Author:郭事业 |
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Description: 运用于人脸识别中的特征提取方法：通过稀疏化特征向量（即使一些不重要的特征值为0），来减少运算量-Used in face recognition feature extraction methods: by sparse feature vector (even if some important features of value 0), to reduce the computation Platform: |
Size: 1878016 |
Author:wangjia |
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Description: 计算机人脸识别技术( Face Reocgnition)就利用计算机分析人脸图像,从中提取出有效的识别信息,用来辨认身份的一门技术。[ 1 ]即对已知人脸进行标准化处理后,通过某种方法和数据库中的人脸样本进行匹配,寻找库中对应人脸及该人脸相关信息。人脸自动识别系统有两个主要技术环节,一是人脸定位,即从输入图像中找到人脸存在的位置,将人脸从背景中分割出来,二是对标准化后的人脸图像进行特征提取和识别。本文中介绍的PCA (特征脸)方法就是一种常用的人脸
特征提取方法。-Computer Face Recognition Technology (Face Reocgnition) on the use of computer analysis of facial image, to extract the valid identification information used to identify the status of a technology. [1] that is known to standardize treatment of face, through a method and a database of face samples for matching, search library, the corresponding face and the face-related information. Automatic face recognition system has two main technical aspects, first, face location, that is, from the input image to find the location of the face there, the faces will be split out from the background, the second is, the standard features of face images extraction and recognition. Described in this paper PCA (Eigenfaces) method is a common facial feature extraction method. Platform: |
Size: 224256 |
Author:Highjoe |
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Description: An automatic facial feature extraction method is
presented in this paper. The method is based on the edge
density distribution of the image. In the preprocessing stage a
face is approximated to an ellipse, and genetic algorithm is
applied to search for the best ellipse region match. In the
feature extraction stage, genetic algorithm is applied to
extract the facial features, such as the eyes, nose and mouth,
in the predefined sub regions. The simulation results
validates that the proposed method is capable of
automatically extracting features from various video images
effectively under natural lighting environments and in the
presence of certain amount of artificial noise and of multi-
face oriented with angles.-An automatic facial feature extraction method is
presented in this paper. The method is based on the edge
density distribution of the image. In the preprocessing stage a
face is approximated to an ellipse, and genetic algorithm is
applied to search for the best ellipse region match. In the
feature extraction stage, genetic algorithm is applied to
extract the facial features, such as the eyes, nose and mouth,
in the predefined sub regions. The simulation results
validates that the proposed method is capable of
automatically extracting features from various video images
effectively under natural lighting environments and in the
presence of certain amount of artificial noise and of multi-
face oriented with angles. Platform: |
Size: 324608 |
Author:fais |
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Description: The aim of the project was to propose a method for reliable detection and extraction of
facial features in two and three dimensions.
Several 2D methods were attempted – edge detection, intensity scanning, and Gabor
transform. The Gabor transform method included much filtering. The edge detection and
intensity scanning methods were largely unreliable. The Gabor transform method was
highly reliable under controlled conditions, and about 60 reliable under uncontrolled
conditions, when it was tested on the Yale Database.
3D methods were attempted, but were never successfully implemented.
The 2D aim was largely met by the Gabor transform method.
The aim was not fulfilled in 3D.
The aim in terms of extraction was fulfilled by an anthropometrical method based on the
work of Kwok-Wai Wong et al.
3-The aim of the project was to propose a method for reliable detection and extraction of
facial features in two and three dimensions.
Several 2D methods were attempted – edge detection, intensity scanning, and Gabor
transform. The Gabor transform method included much filtering. The edge detection and
intensity scanning methods were largely unreliable. The Gabor transform method was
highly reliable under controlled conditions, and about 60 reliable under uncontrolled
conditions, when it was tested on the Yale Database.
3D methods were attempted, but were never successfully implemented.
The 2D aim was largely met by the Gabor transform method.
The aim was not fulfilled in 3D.
The aim in terms of extraction was fulfilled by an anthropometrical method based on the
work of Kwok-Wai Wong et al.
3 Platform: |
Size: 910336 |
Author:brahmia djaber |
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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: 首先利用人脸的色彩特征和自适应阈值法实现特征候选区域和人脸肤色区域的分离，然后利用人脸的几何特性将连通的特征候选
区域保留下来作为人脸特征区域。后续的特征提取可以在这些人脸特征区域中完成。一般的人脸特征提取方法都可以将该方法作为提高效
率的预处理操作。实验证明，该方法具有高效率、低计算量的特点，并且受人脸表情、图像角度和背景的影响较小。-First, the use of the color characteristics of the human face and adaptive thresholding feature candidate region and the skin color of the face area of separation, then the use of the geometric characteristics of the face to the connectivity features of candidate regions preserved as a facial feature region. Subsequent feature extraction can be completed in the area of these facial feature. General facial feature extraction methods the method as a pre-processing operations to improve efficiency. The experiments show that this method has the characteristics of high efficiency, low computational smaller, and the facial expression, the image angle and background. Platform: |
Size: 108544 |
Author:东方 |
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Description: 利用MATLAB对脸部特征进行降维提取，数据齐全，可以直接运行。-Using MATLAB to reduce the dimension of facial features extraction, data is complete, can be run directly. Platform: |
Size: 6667264 |
Author:马振磊 |
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Search - facial features extraction