Face Alignment by 2.5D Active Appearance Model Optimized by Simplex

Abstract

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 embedded system makes it a time consuming algorithm as well. Our 2.5D AAM is generated by taking 3D landmarks from frontal and profile view and 2D texture only from frontal view of the face image. Moreover we propose Nelder Mead Simplex technique for face search. It does not require large memory, thus becoming suitable for embedded systems by eliminating the excess memory and access time requirements. We illustrate 2.5D AAM optimized by Simplex for pose estimation and test it on three databases: M2VTS, synthetic images and webcam images. Results validate our combination of simplex and AAM in 2.5D.