Introduction to Emotion Detection and Predictive Psychology in the Age of Technology

Introduction to Emotion Detection and Predictive Psychology in the Age of Technology

Vaibhav Prakash Vasani, Umesh Chandra, Gayatri Sahu, Srinivasulu Boyineni, S. Dhamodaran, Makhan Kumbhkar, Mritunjay Rai, Swati Gupta
Copyright: © 2024 |Pages: 16
DOI: 10.4018/979-8-3693-1910-9.ch001
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Abstract

A performance study of a deep unified model for a facial expression system using hybrid models like ResNet and DenseNet in a CNN-based image classification framework is crucial. Assessing the effectiveness of this complex system that interprets facial expressions to identify emotions is crucial. Data is carefully prepared and separated to produce a balanced and representative dataset. The hybrid ResNet-DenseNet model architecture is crucial. It must be customised for face expression recognition. Data augmentation, hyper parameter adjustment, and regularisation are used to optimise model performance during training. Accuracy, precision, recall, F1-score, and confusion matrix are used to evaluate the model's performance. A different dataset may be used to evaluate its generalisation skills. Comparing the model to others might reveal its performance.
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1. Introduction

1.1 Emotion Detection

Emotion detection, a burgeoning field in the realm of artificial intelligence and technology, has made remarkable strides in understanding and interpreting human emotions. Through the utilization of advanced machine learning algorithms and data-driven insights, this technology can discern emotional states by analyzing a variety of cues, including facial expressions, voice tone, and text sentiment. It has found applications in numerous domains, from marketing and customer service to mental health and human-computer interaction. Emotion detection not only aids in gauging customer satisfaction, but also in providing personalized mental health support and improving user experiences in technology interfaces. However, it is essential to address the ethical concerns surrounding privacy and consent, as well as to ensure that the technology is used responsibly and with sensitivity to cultural and individual differences. As emotion detection continues to evolve it holds great potential for enhancing human well-being and understanding, provided it is harnessed conscientiously as shown in fig1.

Figure 1.

Emotion detection

979-8-3693-1910-9.ch001.f01

1.2 Predictive Psychology

Predictive psychology is a subfield of psychology that focuses on the development and application of predictive models and data-driven approaches to understand, anticipate, and influence human behavior and mental processes. It leverages a wide range of statistical and computational techniques, such as machine learning and predictive analytics, to make forecasts about future behavior, outcomes, and trends, based on historical data and psychological principles. These predictions can have applications in various domains, including clinical psychology, education, organizational behavior, and consumer behavior, aiming to enhance decision-making, interventions, and strategies to promote positive outcomes and well-being.

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