Pose-Invariant Face Recognition in Videos for Human-Machine Interaction

Bogdan Raducanu¹ and Fadi Dornaika^{2, 3}

¹Computer Vision Center, 08193, Bellaterra, Barcelona, Spain
bogdan@cvc.uab.es

²University of the Basque Country UPV/EHU, San Sebastian, Spain
fadi_dornaika@ehu.es

³IKERBASQUE, Basque Foundation for Science, Bilbao, Spain

Abstract. 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.

LNCS 7584, p. 566 ff.

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