This book focuses on a development for assessing mental changes using eye pupil reactions, namely extracting emotional change from the response to evaluate the viewer's interest in visual information. The pupil of the eye reacts to both brightness and emotional state, including interest, enjoyment, and mental workload. Because pupillary change is a biological signal, various artifacts influence measurements of eye images. Technical procedures are required to extract mental activities from pupillary changes, and they are summarized here step by step, although some procedures contain earlier techniques such as analog video processing.

This study examines the possibility of estimating the viewer's interest and enjoyment of viewing movies by measuring the dynamic pupillary changes, blinking, and subjective interest responses. In evaluation of pupil size, there was a significant difference in pupil size between the higher and the lower shot for the degree of subject interest response in each kind of movies.

The first part of the book shows a pupil reaction model for brightness changes to extract mental activities. Pupil reactions were observed for various visual stimuli in brightness changes. With regard to the characteristics of pupillary changes, a model with a three-layer neural network was developed and the performance was evaluated. Characteristics of pupil reactions during model development are summarized here.

The second part examines the possibility of estimating the viewer's interest and enjoyment of television programs by measuring dynamic pupillary changes, blinking, and subjective interest responses.

The final part describes a development of estimation model of pupil size for blink artifact. The model development was able to estimate pupillary changes and pupil size while the viewer was blinking and was applied to pupillary changes in viewing television programs.

Presents the mental activity assessment procedure using pupil size for emotional changes in viewing movies Discusses pupillary psychological responses and potentials of interests in addition to the basic measurements Explains that the modeling technique for pupillary responses is based on a conventional machine learning procedure

Autorentext

Minoru Nakayama is Professor at Tokyo Institute of Technology.

Yasutaka Shimizu is Professor Emeritus at Tokyo Institute of Technology.

Inhalt
Controlling the Effects of Brightness on the Measurement of Pupil Size as a Means of Evaluating Mental Activity.- Pupil Reaction Model using a Neural Network for Brightness Change.- A Neural-Network Based Eye Pupil Reaction Model for Use with Television Programs.- The Relationship between Pupillary Changes and Subjective Indices to the Content of Television Programs.- An Estimation Model for Pupil Size of Blink Artifacts while Viewing TV Programs