Image complexity analysis with scanpath identification using remote gaze estimation model.

Abstract

Analysis of gaze points has been a vital tool for understanding varied human behavioral pattern and underlying psychological processing. Gaze points are analyzed generally in terms of two events of fixations and saccades that are collectively termed as scanpath. Scanpath could potentially establish correlation between visual scenery and human cognitive tendencies. Scanpath has been analyzed for different domains that include visual perception, usability, memory, visual search or low level attributes like color, illumination and edges in an image. Visual search is one prominent area that examines scanpath of subjects while a target object is searched in a given set of images. Visual search explores behavioral tendencies of subjects with respect to image complexity. Complexity of an image is governed by spatial, frequency and color information present in the image. Scanpath based image complexity analysis determines human visual behavior that could lead to development of interactive and intelligent systems. There are several sophisticated eye tracking devices and associated algorithms for recording and classification of scanpath. However, in the present scenario when the chances of viral infections (COVID-19) from known and unknown sources are high, it is very important that the contact less methods and models be designed. In addition, even though the devices acquire and process eye movement data with fair accuracy but are intrusive and costly. The objective of current research work is to establish the complexity of the given set of images while target objects are searched and to present analysis of gaze search pattern. To achieve these objectives a remote gaze estimation and analysis model has been proposed for scanpath identification and analysis. The model is an alternate option for gaze point tracking and scanpath analysis that is non intrusive and low cost. The gaze points are tracked remotely as against sophisticated wearable eye tracking devices available in the market. The model employs easily available softwares and hardware devices. In the current work, complexity is derived on the basis of analysis of fixation and saccade gaze points. Based on the results generated by the proposed model, influence on subjects due to external stimuli is studied. The set of images chosen, act as external stimuli for the subjects during visual search. In order to statistically analyze scanpath for different subjects, certain scanpath parameters have been identified. The model maps and classifies eye movement gaze points into fixations and saccades and generates data for identified parameters. For eye detection and subsequent iris detection voila jones and circular hough transform (CHT) algorithms have been used. Identification by dispersion threshold (I-DT) is implemented for scanpath identification. The algorithms are customized for better iris and scanpath detection. Algorithms are developed for gaze screen mapping and classification of fixations and saccades. The experimentation has been carried on different subjects. Variations during visual search have been observed and analyzed. The present model requires no contact of human subject with any equipment including eye tracking devices, screen or computing devices.