Eigenfaces - Eigenfaces or PCA approach is nothing more than the mathematical eigenvectors of the face, applied on the dataset images(training). Each eigenface is a point in high dimensional image space converted into point in eigen subspace. They are mutually orthogonal vectors. A blog post dedicated to Eigenfaces can be found here. suffer from- luminance and pose variations.
Gabor Wavelets - They are a family of sinosudol waves which work very similar to how our visual cortex perceives images. It works on the principal of kernel convolution, where the kernels are the Gabor Filters[ differing in lambda,theta,phase,etc. ]
strength- They are tailor made to handle cases of partial occlusion and are also pose invariant.
However Gabor Wavelets have a better model, log-Gabor, which is quite the new thing in feature extraction methods available nowadays.
Local Binary Patterns - This technique involves playing with histograms. It is the next big thing after Gabor. They preserve spatial information and locality. Each pixel is threshold ed with its nearest pixels[no. depends on radius of neighbourhood considered]. A bit vector is obtained, which is labelled and stored in a histogram. All the histograms of windows are then concatened to form a feature vector.
Harr Classifiers - They are cascade classifier used for face detection. They are general purpose classifiers which can to trained to detect faces, or even eyes, ears and noses separately.
Gabor Wavelets - They are a family of sinosudol waves which work very similar to how our visual cortex perceives images. It works on the principal of kernel convolution, where the kernels are the Gabor Filters[ differing in lambda,theta,phase,etc. ]
strength- They are tailor made to handle cases of partial occlusion and are also pose invariant.
However Gabor Wavelets have a better model, log-Gabor, which is quite the new thing in feature extraction methods available nowadays.
Local Binary Patterns - This technique involves playing with histograms. It is the next big thing after Gabor. They preserve spatial information and locality. Each pixel is threshold ed with its nearest pixels[no. depends on radius of neighbourhood considered]. A bit vector is obtained, which is labelled and stored in a histogram. All the histograms of windows are then concatened to form a feature vector.
Harr Classifiers - They are cascade classifier used for face detection. They are general purpose classifiers which can to trained to detect faces, or even eyes, ears and noses separately.
