Pose invariant matching is a very important and challenging problem with various applications like recognizing faces in uncontrolled scenarios, matching objects taken from different view points, etc. In this paper, we propose a discriminative pose-free descriptor (DPFD) which can be used to match faces/objects across pose variations. Training examples at very few representative poses are used to generate virtual intermediate pose subspaces. An image or image region is then represented by a feature set obtained by projecting it on all these subspaces and a discriminative transform is applied on this feature set to make it suitable for classification tasks. Finally, this discriminative feature set is represented by a single feature vector, termed as DPFD. The DPFD of images taken from different viewpoints can be directly compared for matching. Extensive experiments on recognizing faces across pose, pose and resolution on the Multi-PIE and Surveillance Cameras Face datasets and comparisons with state-of-the-art approaches show the effectiveness of the proposed approach. Experiments on matching general objects across viewpoints show the generalizability of the proposed approach beyond faces.
Face recognition,Object recognition,Pose invariant matching,Metric learning,Canonical correlation,Subspace to point representation.
Generalizability theory,Computer vision,Feature vector,Pattern recognition,Computer science,Viewpoints,3D pose estimation,Linear subspace,Artificial intelligence,Object matching,Invariant (mathematics),Discriminative model