Adaptive Object Recognition Using Context-Aware Genetic Algorithm Under Dynamic Environment

Abstract

Adaptation to dynamically changing environment is very important since advanced applications become pervasive and ubiquitous. This paper addresses a novel method of adaptive object recognition using environmental context-awareness and genetic algorithm and t-test. The proposed method tries to distinguish the category of input environment and decides an optimal classifier combination structure accordingly by GA and t-test. It stores its experiences in terms of the data context categories and the evolved artificial chromosomes so that the evolutionary knowledge can be used later. The proposed method has been evaluated in the area of face recognition. Most previous face recognition schemes define their system structures at the design phases, and the structures are not adaptive during operation. Such approaches usually show vulnerability under varying illumination environment. The context-awareness, modeling and identification of input data as context categories, is carried out by Fuzzy ART. The face data context is described based on the image attributes of light direction and brightness. The superiority of the proposed system is shown using four data sets: Inha, FERET and Yale database.