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 set-based 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 have observed that the proposed method with li...

• Type: Journal
• Year: 2010
• Where: ACCV
• Authors: Ujwal D. Bonde, Tae-Kyun Kim, K. R. Ramakrishnan
• Keywords: ACCV 2010, Computer Vision, Face Image, Manifolds, Set-based Face Recognition