Randomised Manifold Forests for Principal Angle-Based Face Recognition

Abstract

In set-based face recognition, each set of face images is often represented as a linear/nonlinear manifold and the Principal Angles (PA) or Kernel PAs are exploited to measure the (dis-)similarity between manifolds. This work systemically evaluates the effect of using different face image representations and different types of kernels in the KPA setup and presents a novel way of randomised learning of manifolds for setbased face recognition. First, our experiments show that sparse features such as Local Binary Patterns and Gabor wavelets significantly improve the accuracy of PA methods over 'pixel intensity'. Combining different features and types of kernels at their best hyper-parameters in a multiple classifier system has further yielded the improved accuracy. Based on the encouraging results, we propose a way of randomised learning of kernel types and hyper-parameters by the set-based Randomised Decision Forests. We observed that the proposed method with linear kernels efficiently competes with those of nonlinear kernels. Further incorporation of discriminative information by constrained subspaces in the proposed method has effectively improved the accuracy. In the experiments over the challenging data sets, the proposed methods improve the accuracy of the standard KPA method by about 35 percent and outperform the Support Vector Machine with the set-kernels manually tuned.