Learning Local Binary Patterns with Enhanced Boosting for Face Recognition

Due to its invariance to monotonic grayscale transformation and simple computation, Local Binary Pattern (LBP) is broadly used as feature extractor in face recognition tasks in recent years [3]. In previous work, people have proposed methods of using Adaboost to select most representative features in samples. Zhang et al. proposed a method applying Adaboost algorithm to select those most distinctive features from which they extract LBP features. Though LBP features selected by Adaboost represent local textures effectively. Their method, however, neglects exploitation of holistic spatial information in nature of image samples. To solve this problem, we proposed the spatial enhanced multi-level boosing using uniform LBP and multilevel Adaboost algorithm. In this paper, we select most distinctive features which then being concatenated to represent spatial information using multi-level boosting algorithm. Experiments on ORL database yielded an exciting recognition rate of 98.96%.