Detection of AI Empathy Using Deep Learning

Deep learning enables the learning of detailed representations of emotions and facial expressions that can be challenging to recognize using conventional machine learning methods. Deep learning algorithms have demonstrated higher accuracy rates in tasks requiring emotion recognition than traditional...

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Bibliographic Details
Published in:	2023 International Conference on Computer Science and Emerging Technologies (CSET) pp. 1 - 5
Main Authors:	Vinay, Namburi Venkat, Kumar, Charugulla Santhosh, Asipalli, Jayanth, Mouli, Metturu, Sindhu Madhuri, G.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 10-10-2023
Subjects:	Computational modeling Deep learning Emotion Detection Emotion recognition Face recognition Human computer interaction Machine learning algorithms Mental health MTCNN (Multi-Task Cascaded Convolutional Neural Network) ResNet
Online Access:	Get full text
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Summary:	Deep learning enables the learning of detailed representations of emotions and facial expressions that can be challenging to recognize using conventional machine learning methods. Deep learning algorithms have demonstrated higher accuracy rates in tasks requiring emotion recognition than traditional machine learning methods. Deep learning models can be customized to each person, enabling more precise emotion recognition based on each person's individual facial expressions. Emotion detection aims to identify and measure human emotions using various physiological, behavioural, and computational techniques. Deep learning algorithms and computer vision techniques are used to assess facial expressions, vocal cues, and other physiological information to ascertain a person's emotional state. Emotion detection has a wide range of possible uses, from mental health diagnosis and treatment to enhancing the effectiveness of virtual assistants and strengthening user interactions with computers. Technology developments and the growing accessibility of massive datasets have improved emotion recognition, making possible new prospects for research and growth in the field. This abstract provides a comprehensive overview of the current state of the art in emotion detection and its applications in various domains.
DOI:	10.1109/CSET58993.2023.10346939