Human implicit intent transition detection based on pupillary analysis

Interpretation of human implicit intention is crucial in the development of an efficient nonverbal human computer interaction system. According to cognitive visuo-motor theory, the human eye movements and pupillary responses are rich source of information about the human intention and behavior. It has been observed that under conditions of constant illumination and accommodation, pupil size varies systematically in relation to a variety of physiological and psychological factors, such as level of mental effort. It is well known that pupillary responses could be used to measure the differences in cognitive load under various tasks. In this paper, we try to detect the transition between the different human implicit intents based on the pupil state analysis. In real-world environment, the pupillary response can be influenced by various external factors like intensity and size of the image. To overcome the influence of the external factors, we develop a robust baseline model. The proposed approach detects the transition of the human's implicit intent from navigational intent to informational intent and vice versa during a visual stimulus. The approach also detects the transition among the different states of the informational intent such as informational intent generation, informational intent maintenance and informational intent disappear.