A Context-Aware Statistical Ontology Approach for Adaptive Face Recognition

This paper proposes a statistical ontology approach for adaptive face recognition in a situation variant environment. In this paper, we have introduced a new concept statistical ontology for context sensitivity, as we found many developed systems work on context invariant environment. Due to the effects of illumination makes a supreme obstinate designing context sensitive recognition system; we have given more emphasis to design such a context variant system using statistical ontology. Ontology can be defined as an explicit specification of conceptualization of a domain typically captured in an abstract model of how people think about things in the domain. Human produces ontologies to understand and explain underlying principle and environment. In this research, we have proposed context ontology, context modeling, context adaptation, and context categorization to design ontology based on illumination criteria. After selecting proper domain of ontology, we have benefited to select a set of action that produces better performance on that domain. We have carried out extensive experiments on these concepts in the region of object recognition in dynamic changing environment and we have achieved an enormous success to proceed for our basic concepts.