Face Classification Using Gabor Wavelets and Random Forest

This paper presents a new face classification technique based on Gabor wavelets and random forest. Random forest is a tree based classifier that consists of many decision trees. Each tree gives a classification and the output is the aggregate of these classifications. The proposed algorithm first extracts features from the face images using Gabor wavelet transform and then uses the random forest algorithm to classify the images based on the extracted features. But Gabor wavelet transform leads to high feature dimensions which increases the cost of computation. The proposed algorithm uses a random forest which selects a small set of most discriminant Gabor wavelet features. Only this small set of features is now used to classify the images resulting in a fast face recognition technique.