Facial expression analysis and expression-invariant face recognition by manifold-based synthesis

Over the last decades, expression classification and face recognition have received substantial attention in computer vision and pattern recognition with more recent efforts focusing on understanding and modelling expression variations. In this paper, we present an expression classification and expression-invariant face recognition method by synthesising photorealistic expression manifolds to expand the gallery set. By means of synthesising expression images from neutral faces, more within-subject variability can be obtained. Eigentransformation is utilised to generate both shape and expression details for novel subjects. Expression classification and face recognition are then performed on the extended training set with synthesised expressions. Experimental results on various datasets show that the proposed method is robust for recognising various expressions and faces with varying degrees of expression. Comprehensive experiments conducted and comparisons with the existing methods are reported. Cross-database synthesis and effect of landmark quality are also studied.