The analysis of frequency domain characteristics of emotional images in eye-tracking experiment

Boqiang Fan; Huimin Ma; Xiang Wang

doi:10.1117/12.2196909

6 July 2015 The analysis of frequency domain characteristics of emotional images in eye-tracking experiment

Boqiang Fan, Huimin Ma, Xiang Wang

Proceedings Volume 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015); 96311O (2015) https://doi.org/10.1117/12.2196909
Event: Seventh International Conference on Digital Image Processing (ICDIP15), 2015, Los Angeles, United States

Abstract

Although recently eye-tracking method has been introduced into behavioral experiments based on dot-probe paradigm, some characteristics in eye-tracking data do not draw as much attention as traditional characteristics like reaction time. It is also necessary to associate eye-tracking data to characteristics of images shown in experiments. In this research, new variables, such as fixation length, times of fixation and times of eye movement, in eye-tracking data were extracted from a behavioral experiment based on dot probe paradigm. They were analyzed and compared to traditional reaction time. After the analysis of positive and negative scenery images, parameters such as hue frequency spectrum PAR (Peak to Average Ratio) were extracted and showed difference between negative and positive images. These parameters of emotional images could discriminate scenery images according to their emotions in an SVM classifier well. Besides, it was found that images’ hue frequency spectrum PAR is obviously relevant to eye-tracking statistics. When the dot was on the negative side, negative images’ hue frequency spectrum PAR and horizontal eye-jumps confirmed to hyperbolic distribution, while that of positive images was linear with horizontal eye-jumps. The result could help to explain the mechanism of human’s attention and boost the study in computer vision.

Citation Download Citation

Boqiang Fan, Huimin Ma, and Xiang Wang "The analysis of frequency domain characteristics of emotional images in eye-tracking experiment", Proc. SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015), 96311O (6 July 2015); https://doi.org/10.1117/12.2196909

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KEYWORDS

Analytical research

Computer vision technology

Machine vision

Diagnostics

Eye

Human vision and color perception

Image analysis

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