GAGM-AAM: A GENETIC OPTIMIZATION WITH GAUSSIAN MIXTURES FOR ACTIVE APPEARANCE MODELS

Abstract : This paper proposes an optimization technique of genetic algorithm (GA) combined with Gaussian mixtures (GAGM) to make a robust, efficient and real time face alignment application for embedded systems. It uses 2.5D Active Appearance Model (AAM) for the face search, the model is generated by taking 3D landmarks and 2D texture of the face image. 3D face alignment requires to optimize 6DOF (Degrees of Freedom) pose and appearance parameters of AAM. These parameters span in a huge face search space. In order to optimize them GA (due to its exploration property) is taken as an optimization technique, but unfortunately it suffers from massive computations. Thanks to the clustering of appearance parameters by Gaussian Mixture, GA optimization becomes time efficient and accurate. We compare it with other technique of simplex, which is found to be more efficient than classical AAM.
Complete list of metadatas

https://hal-supelec.archives-ouvertes.fr/hal-00334604
Contributor : Myriam Andrieux <>
Submitted on : Monday, October 27, 2008 - 1:49:18 PM
Last modification on : Friday, November 16, 2018 - 1:24:28 AM

Identifiers

  • HAL Id : hal-00334604, version 1

Citation

Abdul Sattar, Yasser Aidarouss, Renaud Séguier. GAGM-AAM: A GENETIC OPTIMIZATION WITH GAUSSIAN MIXTURES FOR ACTIVE APPEARANCE MODELS. International Conference on Image Processing, Oct 2008, United States. 4 p. ⟨hal-00334604⟩

Share

Metrics

Record views

269