2016/12/30 by Zhenhua Feng, Zhen-Hua Feng, Josef Kittler +7 · 2 citations
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face and Expression Recognition #Face recognition and analysis #Generative Adversarial Networks and Image Synthesis #cs.CV
paper · pdf · doi:10.48550/arxiv.1612.09548
openalex publication_date 2016/12/30 · arxiv created 2017/06/13 · arxiv updated 2017/06/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Appearance variations result in many difficulties in face image analysis. To deal with this challenge, we present a Unified Tensor-based Active Appearance Model (UT-AAM) for jointly modelling the geometry and texture information of 2D faces. For each type of face information, namely shape and texture, we construct a unified tensor model capturing all relevant appearance variations. This contrasts with the variation-specific models of the classical tensor AAM. To achieve the unification across pose variations, a strategy for dealing with self-occluded faces is proposed to obtain consistent shape and texture representations of pose-varied faces. In addition, our UT-AAM is capable of constructing the model from an incomplete training dataset, using tensor completion methods. Last, we use an effective cascaded-regression-based method for UT-AAM fitting. With these advancements, the utility of UT-AAM in practice is considerably enhanced. As an example, we demonstrate the improvements in training facial landmark detectors through the use of UT-AAM to synthesise a large number of virtual samples. Experimental results obtained using the Multi-PIE and 300-W face datasets demonstrate the merits of the proposed approach.