Face Alignment Through 2.5D Active Appearance Models

Supplementary Material Generative Face Alignment Through 2.5D Active Appearance Models

Generative face alignment through 2.5D active appearance models

This work addresses the matching of a 3D deformable face model to 2D images through a 2.5D Active Appearance Models (AAM). We propose a 2.5D AAM that combines a 3D metric Point Distribution Model (PDM) and a 2D appearance model whose control points are defined by a full perspective projection of the PDM. The advantage is that, assuming a calibrated camera, 3D metric shapes can be retrieved from...

Face Alignment by 2.5D Active Appearance Model Optimized by Simplex

In this paper we propose an efficient algorithm to align the face in real time, based on Active Appearance Model (AAM) in 2.5D. The main objective is to make a robust, rapid and memory efficient application suitable for embedded systems, so they could align the pose rapidly by using less memory. Classical AAM is a high memory consumer algorithm, consequently transfer of this stored memory in an...

Active illumination and appearance model for face alignment

Illumination conditions have an explicit effect on the performance of face recognition systems. In particular, varying the illumination upon the face imposes such complex effects that the identification often fails to provide a stable performance level. In this paper, we propose an approach integrating face identity and illumination models in order to reach acceptable and stable face recognitio...

Real-time View-based Face Alignment using Active Wavelet Networks

The Active Wavelet Network (AWN) [9] approach was recently proposed for automatic face alignment, showing advantages over Active Appearance Models (AAM), such as more robustness against partial occlusions and illumination changes. In this paper, we (1) extend the AWN method to a view-based approach, (2) verify the robustness of our algorithm with respect to unseen views in a large dataset and (...