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Classifying many-class high-dimensional fingerprint datasets using random forest of oblique decision trees

Classifying fingerprint images may require an important features extraction step. The scale-invariant feature transform which extracts local descriptors from images is robust to image scale, rotation and also to changes in illumination, noise, etc. It allows to represent an image in term of the comfortable bag-of-visual-words. | Classifying many-class high-dimensional fingerprint datasets using random forest of oblique decision trees