Publication Type : Conference Proceedings
Publisher : 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE
Source : 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, Kanpur, India (2020)
Url : https://ieeexplore.ieee.org/abstract/document/8944564
Keywords : Classification algorithms, Emotion recognition, emotional category, eye movement tracking, Eye Tracking, Feature extraction, fixation count, fixation frequency, gaze tracking, human bio signals, Human computer interaction, image classification, image detection, learning (artificial intelligence), machine learning approach, neutral images, saccade count, saccade frequency, Scene classification, Scene valence, Support vector machines, Tracking, unpleasant images, Visualization
Campus : Bengaluru
School : Department of Computer Science and Engineering, School of Engineering
Department : Computer Science
Year : 2020
Abstract : Studying human bio signals such as eye movements and tracking them can help in identifying and classifying the emotional essence of a scene. The existing methods employed to evaluate the reaction of the eyes based on exposure to a scene or image often use a classifier to extract features from eye movements. These extracted features are then evaluated to determine the valence of a scene. On the contrary, as much as eye movement has proved to be a reliable source in scene or image detection, factors such as how each feature affects the outcome of the prediction have not been explored. For the determination of the emotional category of images using eye movements, images are categorized into pleasant, neutral and unpleasant images and then these images are shown to the test subjects to record their response. Features of eye movement like fixation count, fixation frequency, saccade count, and saccade frequency among others, along with a machine learning approach was used for scene classification.
Cite this Research Publication : S. Tamuly, Jyotsna C, and Amudha J., “Tracking Eye Movements To Predict The Valence of A Scene”, 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, Kanpur, India, 2020.