Emotion recognition with AI: Techniques and applications
1 Department of CSE, Government Polytechnic Harihar, Karnataka, India.
2 Department of CSE Government Polytechnic Koppal, Karnataka, India.
Review Article
World Journal of Advanced Research and Reviews, 2020, 08(02), 344–352
Publication history:
Received on 21 October 2020; revised on 25 November 2020; accepted on 28 November 2020
Abstract:
Emotion recognition using artificial intelligence (AI) has become an increasingly vital area of research, offering transformative applications across healthcare, human-computer interaction, marketing, education, and entertainment. This paper provides a comprehensive review of the techniques and applications of AI in emotion recognition. Key methodologies, including facial expression analysis, speech emotion recognition, text-based emotion detection, physiological signal analysis, and multimodal approaches, are explored in detail. Each technique's underlying algorithms, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and natural language processing (NLP) methods, are examined for their efficacy in identifying human emotions. This paper also delves into practical applications, showcasing how emotion recognition enhances mental health monitoring, improves user experience, optimizes customer service, personalizes education, and enriches entertainment media. Moreover, this study addresses the ethical and technical challenges associated with emotion recognition, such as data privacy concerns, potential biases in AI models, and the need for accuracy and reliability in emotion detection systems.
The discussion extends to future directions, emphasizing the integration of AI with wearable devices, advancements in multimodal systems, and the expansion of emotion recognition technology into new domains. By presenting a thorough analysis of current techniques and applications, this paper aims to highlight the significant impact and potential of AI-driven emotion recognition, paving the way for future innovations and ethical considerations in this dynamic field. The proliferation of Internet of Things (IoT) technology has enabled the development of smart home systems that can enhance home security, energy efficiency, and convenience. This paper presents the design and implementation of an IoT-based smart home security system that utilizes a variety of sensors and actuators to monitor and control various aspects of home security. The proposed system integrates multiple components, including motion detectors, door/window sensors, cameras, smart locks, and a central control unit, all interconnected through a wireless network. The system leverages cloud computing and machine learning techniques to process sensor data, detect potential threats, and initiate appropriate responses. A user-friendly mobile application allows homeowners to monitor and control the security system remotely. The paper discusses the system architecture, hardware and software components, communication protocols, and security considerations. The implemented system demonstrates the feasibility and potential benefits of IoT-based smart home security solutions.
Keywords:
Emotion Recognition; Artificial Intelligence; Facial Expression Analysis; Speech Emotion Recognition; Text-based Emotion Detection Physiological Signal Analysis; Machine Learning; Internet of Things
Full text article in PDF:
Copyright information:
Copyright © 2020 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0