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Face frontalization consists of synthesizing a frontally-viewed face from anarbitrarily-viewed one. The main contribution of this paper is a robust facealignment method that enables pixel-to-pixel warping. The method simultaneouslyestimates the rigid transformation (scale, rotation, and translation) and thenon-rigid deformation between two 3D point sets: a set of 3D landmarksextracted from an arbitrary-viewed face, and a set of 3D landmarksparameterized by a frontally-viewed deformable face model. An important meritof the proposed method is its ability to deal both with noise (smallperturbations) and with outliers (large errors). We propose to model inliersand outliers with the generalized Student's t-probability distributionfunction, a heavy-tailed distribution that is immune to non-Gaussian errors inthe data. We describe in detail the associated expectation-maximization (EM)algorithm that alternates between the estimation of (i) the rigid parameters,(ii) the deformation parameters, and (iii) the Student-t distributionparameters. We also propose to use the zero-mean normalized cross-correlation,between a frontalized face and the corresponding ground-truth frontally-viewedface, to evaluate the performance of frontalization. To this end, we use adataset that contains pairs of profile-viewed and frontally-viewed faces. Thisevaluation, based on direct image-to-image comparison, stands in contrast withindirect evaluation, based on analyzing the effect of frontalization on facerecognition.

Face Frontalization Based on Robustly Fitting a Deformable Shape Model to 3D Landmarks