3D Facial Shape Similarity with Deep Perceptual Representations

Comparing different 3D shapes is challenging due to their irregularities. Motivated by the human visual system mechanism, where the entire 3D geometry is clearly perceived as a series of multiple projections, we propose a novel facial shape similarity measurement using multiview deep perceptual representations. We introduce a multiview disentangling scheme that accurately represents a facial mesh in multiple coordinates and the training strategy with view specificity and regional consistency to reliably train the network with multiple projections. View specificity pertains to the human visual perception to better recognize facial similarity. Regional consistency mitigates regional redundancy among views. Hence, robust perceptual features with respect to views are embedded and accurate similarity can be measured. Consequently, the view-specific integration scheme incorporates the similarities of all views, allowing for highly consistent measurement. The experiments demonstrate that the proposed similarity outperforms state-of-the-arts and significantly improves the details in terms of geometry and human perception.