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Image Recommendation System Based on Environmental and Human Face Information

With the advancement of computer hardware and communication technologies, deep learning technology has made significant progress, enabling the development of systems that can accurately estimate human emotions. Factors such as facial expressions, gender, age, and the environment influence human emot...

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Detalles Bibliográficos
Autores principales: Won, Hye-min, Heo, Yong Seok, Kwak, Nojun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255966/
https://www.ncbi.nlm.nih.gov/pubmed/37300029
http://dx.doi.org/10.3390/s23115304
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author Won, Hye-min
Heo, Yong Seok
Kwak, Nojun
author_facet Won, Hye-min
Heo, Yong Seok
Kwak, Nojun
author_sort Won, Hye-min
collection PubMed
description With the advancement of computer hardware and communication technologies, deep learning technology has made significant progress, enabling the development of systems that can accurately estimate human emotions. Factors such as facial expressions, gender, age, and the environment influence human emotions, making it crucial to understand and capture these intricate factors. Our system aims to recommend personalized images by accurately estimating human emotions, age, and gender in real time. The primary objective of our system is to enhance user experiences by recommending images that align with their current emotional state and characteristics. To achieve this, our system collects environmental information, including weather conditions and user-specific environment data through APIs and smartphone sensors. Additionally, we employ deep learning algorithms for real-time classification of eight types of facial expressions, age, and gender. By combining this facial information with the environmental data, we categorize the user’s current situation into positive, neutral, and negative stages. Based on this categorization, our system recommends natural landscape images that are colorized using Generative Adversarial Networks (GANs). These recommendations are personalized to match the user’s current emotional state and preferences, providing a more engaging and tailored experience. Through rigorous testing and user evaluations, we assessed the effectiveness and user-friendliness of our system. Users expressed satisfaction with the system’s ability to generate appropriate images based on the surrounding environment, emotional state, and demographic factors such as age and gender. The visual output of our system significantly impacted users’ emotional responses, resulting in a positive mood change for most users. Moreover, the system’s scalability was positively received, with users acknowledging its potential benefits when installed outdoors and expressing a willingness to continue using it. Compared to other recommender systems, our integration of age, gender, and weather information provides personalized recommendations, contextual relevance, increased engagement, and a deeper understanding of user preferences, thereby enhancing the overall user experience. The system’s ability to comprehend and capture intricate factors that influence human emotions holds promise in various domains, including human–computer interaction, psychology, and social sciences.
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spelling pubmed-102559662023-06-10 Image Recommendation System Based on Environmental and Human Face Information Won, Hye-min Heo, Yong Seok Kwak, Nojun Sensors (Basel) Article With the advancement of computer hardware and communication technologies, deep learning technology has made significant progress, enabling the development of systems that can accurately estimate human emotions. Factors such as facial expressions, gender, age, and the environment influence human emotions, making it crucial to understand and capture these intricate factors. Our system aims to recommend personalized images by accurately estimating human emotions, age, and gender in real time. The primary objective of our system is to enhance user experiences by recommending images that align with their current emotional state and characteristics. To achieve this, our system collects environmental information, including weather conditions and user-specific environment data through APIs and smartphone sensors. Additionally, we employ deep learning algorithms for real-time classification of eight types of facial expressions, age, and gender. By combining this facial information with the environmental data, we categorize the user’s current situation into positive, neutral, and negative stages. Based on this categorization, our system recommends natural landscape images that are colorized using Generative Adversarial Networks (GANs). These recommendations are personalized to match the user’s current emotional state and preferences, providing a more engaging and tailored experience. Through rigorous testing and user evaluations, we assessed the effectiveness and user-friendliness of our system. Users expressed satisfaction with the system’s ability to generate appropriate images based on the surrounding environment, emotional state, and demographic factors such as age and gender. The visual output of our system significantly impacted users’ emotional responses, resulting in a positive mood change for most users. Moreover, the system’s scalability was positively received, with users acknowledging its potential benefits when installed outdoors and expressing a willingness to continue using it. Compared to other recommender systems, our integration of age, gender, and weather information provides personalized recommendations, contextual relevance, increased engagement, and a deeper understanding of user preferences, thereby enhancing the overall user experience. The system’s ability to comprehend and capture intricate factors that influence human emotions holds promise in various domains, including human–computer interaction, psychology, and social sciences. MDPI 2023-06-02 /pmc/articles/PMC10255966/ /pubmed/37300029 http://dx.doi.org/10.3390/s23115304 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Won, Hye-min
Heo, Yong Seok
Kwak, Nojun
Image Recommendation System Based on Environmental and Human Face Information
title Image Recommendation System Based on Environmental and Human Face Information
title_full Image Recommendation System Based on Environmental and Human Face Information
title_fullStr Image Recommendation System Based on Environmental and Human Face Information
title_full_unstemmed Image Recommendation System Based on Environmental and Human Face Information
title_short Image Recommendation System Based on Environmental and Human Face Information
title_sort image recommendation system based on environmental and human face information
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255966/
https://www.ncbi.nlm.nih.gov/pubmed/37300029
http://dx.doi.org/10.3390/s23115304
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