Experiments on the LFW database using curvelet transforms and a random forest-kNN cascade

Successful recognition of faces from unconstrained complex images is absolutely essential for many biometrics and surveillance applications. This paper aims at exploring the use of the curvelet transform as a method of facial feature extraction and the use of the random forests as a successful classifier. The real value of this paper is its suggested use of a cascade of the random forest classifier with a nearest neighbour verifier. In this framework, the wrapping based curvelet transform is used to extract features, which are then used to train a random forest classifier. A kNN classifier (termed here as a ´verifier´) is cascaded with the random forest to further correct any wrong decisions made by the random forest. On a subset of the Labeled Faces in the Wild dataset, this method performs well with an average percentage recognition of 82%.