Summary about Face Recognition with OpenCV
Some ideas and approachments
geometric feature
- marker points ( position of eyes, ears, noses, ...) are used to build the feature vector (the distance, the angle, ...)
Eigenfaces
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The idea is that a facial image is a point from a high-dim image space, and a high-dim dataset is often described by correlated variables and therefore only a few meaningful dim account for most of the information .
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alg
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PCA alg finds the lower-dim subspace with maximum variance (with the mean) in data. ( that is, to turn a set of possibly correlated variables into a smaller set of uncorrelated variables) .
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There is a TRICK here in computing the eigenvectors: a M x N matrix (M rows, N columns) with M > N can only have N - 1 non-zero eigenvalues, so we can take the eigenvalue decomposition S of size N x N instead .
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The approach maximizes the total scatter, but if the variance is generated by an external source like light, components with a maximum variance over all classes aren't nessarily useful for classification.
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The [API] [http://docs.opencv.org/trunk/modules/contrib/doc/facerec/facerec_api.html] in OpenCV:
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Ptr createEigenFaceRecognizer(int num_components, double threshold)
(~\sources\modules\contrib\src\facerec.cpp)
- Perform the PCA
- (~\modules\core\src\matmul.cpp)
pca(data, Mat(), CV_PCA_DATA_AS_ROW, _num_components)
The parameter num_components means that how many principal components to retain (it is not larger than the number of training samples) .
- Get the feature vectors (y here is the principal component )
Mat y = subspaceProject(_eigenvectors, _mean, data.row(sampleIdx)
The parameter threshold here gives a upper distance limit in the prediction (It is the parameter minDist in the Eigenfaces::predict())
Eigenfaces::predict(InputArray _src, int &minClass, double &minDist)
Each test sample is projected into PCA subspace,and get the related label based on the NORM_L2 distance in the trained subspace.
Fisherfaces
Local Binary Patterns Histograms (LBPH)
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idea
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The focus is only on extracting local features of an object, thus the features in this waywill have a low-dim implicitly.
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Also, the local description has to be a bit robust against image illumination variations (things like scale,translation or rotation), so the Local Binary Patterns (LBP) is given to summarize the local structure in a image by comparing each pixel with its neighborhood .
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The [API] [http://docs.opencv.org/trunk/modules/contrib/doc/facerec/facerec_api.html] in OpenCV Ptr createLBPHFaceRecognizer(int radius,int neighbors, int grid_x, int grid_y, double threshold) Calculate lbp image
- elbp(src[sampleIdx],_radius,_neighbors)
The parameters radius and neighbors are used in the local binary pattern creation
- Get spatial histogram from this lbp image
Mat p = spatial_histogram(
lbp_image, /* lbp_image */
static_cast<int>(std::pow(2.0, static_cast<double>(_neighbors))), /* number of possible patterns */
_grid_x, /* grid size x */
_grid_y, /* grid size y */
true)
The parameters *gird_x* and *grid_y* control the grid size of the spatial histograms.
At last the feature vectors (p here is the spatial histogram) are given .
LBPH::predict(InputArray _src, int &minClass, double &minDist)
compareHist(_histograms[sampleIdx], query, CV_COMP_CHISQR)
Chi-square test is used for the distance measure
The Performance
Reference