Optimizing selection of PZMI features based on MMAS algorithm for face recognition of the online video contextual advertisement user-oriented system

Abstract

Presently, the advertising has been grown to focus on multimedia interactive model with through the Internet. The Online Video Advertisement User-oriented (OVAU) system is combined of the machine learning model for face recognition from camera, multimedia streaming protocols, and video meta-data storage technology. Face recognition is an importance phase which can improve the efficiency performance of the OVAU system. The Feature Selection (FS) for face recognition is solved by MMAS-FS algorithm used PZMI feature. The heuristic information extracted from the selected feature vector as ant’s pheromone. The feature subset optimal is selected by the shortest length features and best presentation of classifier. The experiments were analyzed on face recognition show that our algorithm can be easily applied without the priori information of features. The performance evaluated of our algorithm is better than previous approaches for feature selection.