Orthogonal-Moment-Based Attraction Measurement With Ocular Hints in Video-Watching Task
Bibliographic Data
| ID | 22108221 |
|---|---|
| Authors | Minqiang Yang (0000-0002-7571-6439, Lanzhou University), Xiang Feng (0009-0003-4439-2253, Lanzhou University), Rong Ma (0000-0001-8941-6966, Lanzhou University), Xiongying Li (0000-0002-5477-9828, Lanzhou University), Chengsheng Mao (0000-0002-1515-9626, Northwestern University) |
| Year | 2023 |
| Volume | 10 |
| Issue | 3 |
| Pages | 900-909 |
| Publication date | 2023-06-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2023.3268505 |
| OpenAlex | W4376863496 |
| Language | EN |
| Citations received | 1 |
| References cited | 44 |
Pupil dilation and eye movements are closely related to human emotional and cognitive processes. Visual stimulus, especially video clips, is widely used in computer-assisted experimental paradigms as emotional inducers. However, the level of attraction as a critical factor to such visual stimulus still needs comprehensive investigation. This article conducts a novel study of attraction assessment with pupil diameter and eye movements. We collected high temporal resolution ocular variation data from 50 subjects while they viewed a variety of emotional video stimuli. Besides, this article proposes two orthogonal-moment-based feature extraction methods for emotion classification, i.e., Legendre moment and Krawtchouk moment. The results of experiments show that our proposed feature sets achieve better classification performance compared with conventional time- or frequency-domain feature sets. The accuracy of predicting attraction level reached 87.4% and 91.0% when new features were used alone and combined with conventional features, respectively. Compared with the conventional features with an accuracy of 86.6%, our proposed features can improve the accuracy by 4.4%.This study conducts a ground investigation of quantitative attraction assessment for video clips, which might provide reference for paradigm design of affective computing research
Attraction · Cognitive psychology · Computer vision · Feature extraction · Pupil · Pupil size · Computer Science · Emotion and Mood Recognition · Gaze Tracking and Assistive Technology · Image and Video Quality Assessment · Psychology · Artificial Intelligence
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| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |