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With advances in intelligent technologies, e.g. ambient intelligence, context-aware, and pervasive systems, much research is now devoted to a computational paradigm that senses and perceives changes in human emotion. This paper presents a context-aware architecture for adaptive emotional sensibility analysis called CAF-ESA (a Context-Aware Framework based Emotional Sensibility Analysis) with adaptive capability for use under both diverse changes in both human emotion and the illumination environment. Our proposed system implements context-awareness by a system that identifies working situations as usage contexts. An unsupervised learning algorithm models usage context while a supervised learning algorithm identifies the usage context. A genetic algorithm explores the emotional sensibility space for each identified usage context to determine human eye movement face images. The framework is validated for locating the pupil under changing illumination environments, and for pupil movement that is associated with emotional sensibility such as for both a positive and a negative emotion. We have achieved encouraging experimental results in the real time detection of pupil location and measurement of pupil movement.