Description: Library for creating computer vision systems, using OpenCV, encapsulating the main functionalities on easy to use C++ way, and extending them. It includes face recognition, wavelet transform, stereo vision (future) and speak recognition(future). Platform: |
Size: 1683398 |
Author:廖广军 |
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Description: Library for creating computer vision systems, using OpenCV, encapsulating the main functionalities on easy to use C++ way, and extending them. It includes face recognition, wavelet transform, stereo vision (future) and speak recognition(future).-Library for creating computer vision systems, using OpenCV, encapsulating the main functionalities on easy to use C++ Way, and extending them. It includes face recognition, wavelet transform, stereo vision (future) and speak recognition (future). Platform: |
Size: 2207744 |
Author:廖广军 |
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Description: 基于弹性模板匹配的人脸表情识别程序。首先针对静态表情图像进行表情图像的灰度、尺寸归一化，然后利用Gabor小波变换提取人脸表情特征以构造表情弹性图，最后提出基于弹性模板匹配及K-近邻的分类算法实现人脸表情的识别。-Flexible template matching based on facial expression recognition procedures. First of all, the expression for the static image of the gray-scale face image, size, normalized, and then extracted using Gabor wavelet transform features of human facial expression to expression of elastic graph structure, and finally based on flexible template matching and the K-neighbors classification algorithm Facial Expression identification. Platform: |
Size: 763904 |
Author:hejian |
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Description: PCA人脸识别算法，识别率达到99 ，采用小波变换的方法及主成分分析法。-PCA face recognition algorithm, the recognition rate up to 99 , using wavelet transform methods and principal component analysis. Platform: |
Size: 1024 |
Author:Tiko |
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Description: lda线性特征提取，用于人脸识别，首先进行小波特征提取后用lda提取特征。-lda linear feature extraction for face recognition, first of all, after feature extraction using wavelet feature extraction using lda. Platform: |
Size: 2048 |
Author:gu |
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Description: Wavelet 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 Wavelet 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 Wavelet-based face recognition much more accurate than other approaches.
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: 提出了利用小波变换和余弦变换与BP神经网络相结合的人脸识别方法-Using the wavelet transform and cosine transform and BP neural network face recognition combining Platform: |
Size: 672768 |
Author:夏天 |
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Description: This study proposes a novel near infrared face recognition algorithm based on a combination of both
local and global features. In this method local features are extracted from partitioned images by means
of undecimated discrete wavelet transform (UDWT) and global features are extracted from the whole
face image by means of Zernike moments (ZMs). Spectral regression discriminant analysis (SRDA) is then
used to reduce the dimension of features. In order to make full use of global and local features and
further improve the performance, a decision fusion technique is employed by using weighted sum rule.
Experiments conducted on CASIA NIR database and PolyU-NIRFD database indicate that the proposed
method has superior overall performance compared to some other methods in the presence of facial
expressions, eyeglasses, head rotation, image noise and misalignments. Moreover its computational time
is acceptable for on-line face recognition systems Platform: |
Size: 1038336 |
Author:abdou |
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Search - face recognition using wavelet