A Visual–Audio-Based Emotion Recognition System Integrating Dimensional Analysis

Tian, Jiajia; She, Yingying

doi:10.1109/tcss.2022.3200060

Cited by 8 publications

(3 citation statements)

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“…Currently, there are two main forms of emotion annotation: emotion category labels [18], and dimensional models [19]. Traditional emotion annotation is primarily based on category labels, and there are many classification methods for emotion categories based on different application contexts, such as the seven standard emotions [2] or 20 basic emotions such as boredom, anger, anxiety, surprise, sadness, depression, pride, hope, confusion, and happiness [20].…”

Section: A Emotion Parametersmentioning

confidence: 99%

Multi-Task Emotion Recognition Based on Dimensional Model and Category Label

Huo,

2024

IEEE Access

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This study primarily designs a multitask emotion recognition model that combines Valence, Arousal, Dominance (VAD) three-dimensional continuous emotion analysis and discrete emotion classification, providing a more comprehensive and fine-grained emotional measurement tool for intelligent emotional interaction. It utilizes the correlation constraints between the two recognition tasks (category labels are points in the VAD three-dimensional emotion space) to enhance recognition accuracy. First, it provides a method and dataset for multi-dimensional continuous emotion recognition in the VAD three-dimensional space, which can describe emotional states more comprehensively and finely than traditional fixed emotion category labels, especially in dimension D, which is currently less researched. The integration of the Dominance (D) dimension enables a more comprehensive representation of emotional expressions, capturing nuanced variations related to dominance-related behaviors, particularly in contexts where understanding dominance cues is crucial. Second, because fixed category emotion labels represent a point in the VAD threedimensional space, it utilizes the correlation between them for multi-task joint learning and establishes constraints between emotion categories and the VAD multi-dimensional emotion space. In the experiment, it used the emotional labels available in the existing emotion category dataset FER2013, manually added VAD annotations, and used it for VAD emotional measurement. The prediction results indicate that the average losses for predicting V, A, and D decrease by 0.7%, 6%, and 0.4% respectively, which verifies the effectiveness of the proposed multi-task strategies. The annotated VAD dataset and multi-task emotion recognition codes are available in Github: https://github.com/YeeHoran/Multi-task-Emotion-Recognition.

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Section: A Emotion Parametersmentioning

confidence: 99%

Multi-Task Emotion Recognition Based on Dimensional Model and Category Label

Huo,

2024

IEEE Access

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Section: Introductionmentioning

confidence: 99%

“…MOTION recognition is an important research topic in computational social systems [1][2][3][4][5][6][7][8]. By establishing intelligent computational models, machines can understand human emotions, which leads to a smooth and real human-machine interactions in intelligent social systems [1][2][3][4]. Moreover, emotion recognition can also be applied to social psychological research [5], individual mental state monitoring [6][7] and cognitive science [8].…”

Section: Introductionmentioning

confidence: 99%

Piezoelectric Touch Sensing and Random-Forest-Based Technique for Emotion Recognition

Qi,

Jia,

Feng

et al. 2024

IEEE Trans. Comput. Soc. Syst.

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Emotion recognition, a process of automatic cognition of human emotions, has great potential to improve the degree of social intelligence. Among various recognition methods, Emotion recognition based on touch event's temporal and force information receives global interests. Although previous studies have shown promise in the field of keystroke-based emotion recognition, they are limited by the need for long-term text input and the lack of high-precision force sensing technology, hindering their real-time performance and wider applicability. To address this issue, in this paper, a piezoelectric-based keystroke dynamic technique is presented for quick emotion detection. The nature of piezoelectric materials enables high-resolution force detection. Meanwhile, the data collecting procedure is highly simplified because only the password entry is needed. International Affective Digitized Sounds (IADS) are applied to elicit users ' emotions, and a PAD emotion scale is used to evaluate and label the degree of emotion induction. A Random Forest (RF) based algorithm is used in order to reduce the training dataset and improve algorithm portability. Finally, an average recognition accuracy of 79.37% of 4 emotions (happiness, sadness, fear, disgust) is experimentally achieved. The proposed technique improves the reliability and practicability of emotion recognition in realistic social systems.

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