Contribution

We introduce a method for expression invariant face recognition. A generative 3D Morphable Model (3DMM) is used to separate identity and ex-pression components. The expression removal re-sults in greatly increased recognition performance, even on difficult datasets, without a decrease in performance on expression-less datasets. It is applicable to any kind of input data, and was evaluated here on textureless range scans. Model The Model was learnt from 175 subjects. We used one neutral expression scan per identity and 50 ex-pression scans of a subset of the subjects. The identity model is a linear model build from the neutral scans. f = µ+Mnαn. (1) For each of the 50 expression scans, we calculated an expression vector as the difference between the expression scan and the corresponding neutral scan of that subject. This data is already mode-centered, if we regard the neutral expression as the natural mode of expression data. From these offset vectors an additional expression matrixMe was calculated, such that the complete linear Model is f = µ+Mnαn +Meαe (2) The assumption here is, that the face and expres-sion space are linearly independent, such that each face is represented by a unique set of coefficients. Fitting A Robust Nonrigid ICP method was used to fit the model to the data. Robustness was achieved by iter-atively reweighting the correspondences and using hard compatability test for the closest points. Fitting was initialized by a simple nose detector and proceeded fully automatic. Distance Measure The Mahalanobis angle between the identity coeffi-cients αn was used for classification