Is it really possible to extract Real emotion Systematically?
Yes, Many research works carried out to identify the real emotion and embed the emotion with the physiological signals to correlate the outcome. Such a model utilize various form of data source such as audio data, physiological signals, face expressions, unique face landmarks, gestures and body languages etc.
Automatic extraction of emotion feature have the capability to effectively interact with the computer. Over the decades, optimum research is carried out to improvise the prediction accuracy, reducing the processing delay for robustness of Human-computer interface (HCI)
Mono modal and Multi modal are the types of scenarios utilized for feature extraction prominently explore the uniqueness of the inputs. Deep learning algorithms give promising results on detection process. perhaps, Cross model approach combines the benefit of multi-modal and Mono-modal extraction process.
Cross modal approach enables to read the combinational options to determine the exact emotion. using embedded systems. the real time EEG data also used. EEG determines the brain patterns, Audio is highly recommended input for emotion extraction.
Such Computing process is called Affective Computing in Psychology. Some of the tools used are
- Python,
- Kiros,
- CrowdEmotion and measuring tool such as
- SAM,
- emoji model etc.
The below architecture is the sample Emotion detection process using Affective Computing.
Architecture of affective computing sample Model using CNN [1]
References
[1] J. Han, Z. Zhang, Z. Ren and B. Schuller, "EmoBed: Strengthening Monomodal Emotion Recognition via Training with Crossmodal Emotion Embeddings," in IEEE Transactions on Affective Computing, vol. 12, no. 3, pp. 553-564, 1 July-Sept. 2021, doi: 10.1109/TAFFC.2019.2928297.
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