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Hassan Nataj Solhdar, Mohammad, Erfani majd, Naser, keramatzadeh, Alireza. (1403). Real-Time Age, Gender, and Emotion Detection Using a Guided Module-Based Convolutional Neural Network for Facial Expression Analysis. , 56(2), 41-67. doi: 10.22059/jac.2024.382907.1216
Mohammad Hassan Nataj Solhdar; Naser Erfani majd; Alireza keramatzadeh. "Real-Time Age, Gender, and Emotion Detection Using a Guided Module-Based Convolutional Neural Network for Facial Expression Analysis". , 56, 2, 1403, 41-67. doi: 10.22059/jac.2024.382907.1216
Hassan Nataj Solhdar, Mohammad, Erfani majd, Naser, keramatzadeh, Alireza. (1403). 'Real-Time Age, Gender, and Emotion Detection Using a Guided Module-Based Convolutional Neural Network for Facial Expression Analysis', , 56(2), pp. 41-67. doi: 10.22059/jac.2024.382907.1216
Hassan Nataj Solhdar, Mohammad, Erfani majd, Naser, keramatzadeh, Alireza. Real-Time Age, Gender, and Emotion Detection Using a Guided Module-Based Convolutional Neural Network for Facial Expression Analysis. , 1403; 56(2): 41-67. doi: 10.22059/jac.2024.382907.1216

Real-Time Age, Gender, and Emotion Detection Using a Guided Module-Based Convolutional Neural Network for Facial Expression Analysis

Journal of Algorithms and Computation
دوره 56، شماره 2، اسفند 2024، صفحه 41-67    اصل مقاله (1.32 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jac.2024.382907.1216
نویسندگان
Mohammad Hassan Nataj Solhdar* ؛ Naser Erfani majd؛ Alireza keramatzadeh
Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University of Ahvaz
چکیده
This study presents an innovative approach to real-time facial expression analysis using a guided module-based convolutional neural network. The proposed methodology simultaneously detects emotions, age, and gender with high accuracy, achieving 95.1% for seven facial emotions. The research contributes to various fields, including healthcare, security, and human-computer interaction. A custom real-time dataset encompassing diverse age groups was created to enhance the model's efficiency. The study conducted an ablation analysis to optimize the architecture's effectiveness. Quantitative and qualitative results demonstrate superior performance compared to existing methods across multiple datasets. The proposed approach outperforms six state-of-the-art models in accurately detecting emotions based on age and gender in real-time scenarios. This research advances the development of explainable deep-learning models for emotion recognition, addressing challenges posed by specialized datasets and facilitating more sophisticated systems for real-time human interaction analysis.
کلیدواژه‌ها
Facial expression analysis؛ Convolutional neural network؛ Emotion detection؛ Age recognition؛ Gender classification؛ Guided module-based؛ Fusion network
آمار
تعداد مشاهده مقاله: 34
تعداد دریافت فایل اصل مقاله: 26





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