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%.