Online evolutionary context-aware classifier ensemble framework for object recognition

In this paper, we propose an online evolutionary context-aware classifier ensemble framework for object recognition systems which are adaptive to various environments. The starting point utilizes a context recognizer, a context knowledge base and a classifier ensemble generator to provide optimal solutions for classifier ensemble. Even though our framework is quite general and could be applied to various classification tasks, here we focus on the cases of face recognition. The proposed framework uses an unsupervised learning method to carry out context modeling tasks for various environments. The data for classifier ensemble are assigned to corresponding contexts based on supervised learning and an evolutionary algorithm processes all the information to generate context knowledge for online adaptation. Experimental comparisons with systems based on conventional face recognition algorithms upon four extended benchmark data sets, E-FERET, E-Yale, E-INHA, and our own database showed that the system based on our framework was able to operate in dynamic environments with stable performance which others could not achieve.