PT Unknown
AU Bogdan Raducanu
   Fadi Dornaika
TI Pose-Invariant Face Recognition in Videos for Human-Machine Interaction
BT 12th European Conference on Computer Vision
PY 2012
BP 566.575
VL 7584
DI 10.1007/978-3-642-33868-7_56
AB Human-machine interaction is a hot topic nowadays in the communities of computer vision and robotics. In this context, face recognition algorithms (used as primary cue for a person’s identity assessment) work well under controlled conditions but degrade significantly when tested in real-world environments. This is mostly due to the difficulty of simultaneously handling variations in illumination, pose, and occlusions. In this paper, we propose a novel approach for robust pose-invariant face recognition for human-robot interaction based on the real-time fitting of a 3D deformable model to input images taken from video sequences. More concrete, our approach generates a rectified face image irrespective with the actual head-pose orientation. Experimental results performed on Honda video database, using several manifold learning techniques, show a distinct advantage of the proposed method over the standard 2D appearance-based snapshot approach.
ER