Aligned discriminative pose robust descriptors for face and object recognition

Face and object recognition in uncontrolled scenarios due to pose and illumination variations, low resolution, etc. is a challenging research area. Here we propose a novel descriptor, Aligned Discriminative Pose Robust (ADPR) descriptor, for matching faces and objects across pose which is also robust to resolution and illumination variations. We generate virtual intermediate pose subspaces from training examples at a few poses and compute the alignment matrices of those subspaces with the frontal subspace. These matrices are then used to align the generated subspaces with the frontal one. An image is represented by a feature set obtained by projecting its low-level feature on these aligned subspaces and applying a discriminative transform. Finally, concatenating all the features we generate the ADPR descriptor. We perform experiments on face and object databases across pose, pose and resolution, and compare with state-of-the-art methods including deep learning approaches to show the effectiveness of our descriptor.