Faster Region Convolutional Neural Network(FRCNN)Based Facial Emotion Recognition

作     者：J.Sheril Angel A.Diana Andrushia TMary Neebha Oussama Accouche Louai Saker N.Anand 

作者机构：Department of Electronics and Communication EngineeringKarunya Institute of Technology and SciencesCoimbatore641114India College of Engineering and TechnologyAmerican University of the Middle EastEgaila54200Kuwait Department of Civil EngineeringKarunya Institute of Technology and SciencesCoimbatore641114India 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第79卷第5期

页      面：2427-2448页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The authors would like to express their gratitude for the valuable feedback and suggestions provided by all the anonymous reviewers and the editorial team 

主　　题：Facial emotions FRCNN deep learning emotion recognition face CNN 

摘      要：Facial emotion recognition(FER)has become a focal point of research due to its widespread applications,ranging from human-computer interaction to affective *** traditional FER techniques have relied on handcrafted features and classification models trained on image or video datasets,recent strides in artificial intelligence and deep learning(DL)have ushered in more sophisticated *** research aims to develop a FER system using a Faster Region Convolutional Neural Network(FRCNN)and design a specialized FRCNN architecture tailored for facial emotion recognition,leveraging its ability to capture spatial hierarchies within localized regions of facial *** proposed work enhances the accuracy and efficiency of facial emotion *** proposed work comprises twomajor key components:Inception V3-based feature extraction and FRCNN-based emotion *** experimentation on Kaggle datasets validates the effectiveness of the proposed strategy,showcasing the FRCNN approach’s resilience and accuracy in identifying and categorizing facial *** model’s overall performance metrics are compelling,with an accuracy of 98.4%,precision of 97.2%,and recall of 96.31%.This work introduces a perceptive deep learning-based FER method,contributing to the evolving landscape of emotion recognition *** high accuracy and resilience demonstrated by the FRCNN approach underscore its potential for real-world *** research advances the field of FER and presents a compelling case for the practicality and efficacy of deep learning models in automating the understanding of facial emotions.