Robust Face Frontalization for Visual Speech Recognition

Zhiqi Kang, Radu Horaud, Mostafa Sadeghi; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2021, pp. 2485-2495

Face frontalization consists of synthesizing a frontally-viewed face from an arbitrarily-viewed one. The main contribution of this paper is a robust frontalization method that preserves non-rigid facial deformations, i.e. expressions, to improve lip reading. The method iteratively estimates the rigid transformation (scale, rotation, and translation) and the non-rigid deformation between 3D landmarks extracted from an arbitrarily-viewed face, and 3D vertices parameterized by a deformable shape model. An important merit of the method is its ability to deal with large Gaussian and non-Gaussian errors in the data. For that purpose, we use the generalized Student-t distribution. The associated EM algorithm assigns a weight to each observed landmark, the higher the weight the more important the landmark, thus favoring landmarks that are only affected by rigid head movements. We propose to use the zero-mean normalized cross-correlation (ZNCC) score to evaluate the ability to preserve facial expressions. Moreover, we show that the method, when incorporated into a deep lip-reading pipeline, considerably improves the word classification score on an in-the-wild benchmark.