Abstract
Physical activity, location, as well as a person\textquoterights
psychophysiological and affective state are common dimensions for
developing context-aware systems in ubiquitous computing. An important
yet missing contextual dimension is the cognitive context that comprises
all aspects related to mental information processing, such as perception,
memory, knowledge, or learning. In this work we investigate the feasibility
of recognising visual memory recall. We use a recognition methodology
that combines minimum redundancy maximum relevance feature selection
(mRMR) with a support vector machine (SVM) classifier. We validate
the methodology in a dual user study with a total of fourteen participants
looking at familiar and unfamiliar pictures from four picture categories:
abstract, landscapes, faces, and buildings. Using person-independent
training, we are able to discriminate between familiar and unfamiliar
abstract pictures with a top recognition rate of 84.3% (89.3% recall,
21.0% false positive rate) over all participants. We show that eye
movement analysis is a promising approach to infer the cognitive
context of a person and discuss the key challenges for the real-world
implementation of eye-based cognition-aware systems.
