Augmenting Frontal Face Models for Non-Frontal Verification
Conrad Sanderson, Samy Bengio · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · 2003
In this work we propose to address the problem of non-frontal face verification when only a frontal training image is available (e.g. a passport photograph) by augmenting a client's frontal face model with artificially synthesized models for non-frontal views. In the framework of a Gaussian Mixture Model (GMM) based classifier, two techniques are proposed for the synthesis: UBMdiff and LinReg. Both techniques rely on a priori information and learn how face models for the frontal view are related to face models at a non-frontal view. The synthesis and augmentation approach is evaluated by applying it to two face verification systems: Principal Component Analysis (PCA) based and DCTmod2 [29] based; the two systems are a representation of holistic and non-holistic approaches, respectively. Results from experiments on the FERET database suggest that in almost all cases, frontal model augmentation has beneficial effects for both systems; they also suggest that the LinReg technique (which is based on multivariate regression of classifier parameters) is more suited to the PCA based system and that the UBMdiff technique (which is based on differences between two general face models) is more suited to the DCTmod2 based system. The results also support the view that the standard DCTmod2/GMM system (trained on frontal faces) is less affected by out-of-plane rotations than the corresponding PCA/GMM system; moreover, the DCTmod2/GMM system using augmented models is, in almost all cases, more robust than the corresponding PCA/GMM system.