Deep Facial Emotion Recognition Using Local Features Based on Facial Landmarks for Security System

作     者：Youngeun An Jimin Lee Eunsang Bak Sungbum Pan 

作者机构：IT Research InstituteChosun UniversityGwang-Ju61452Korea 

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

年 卷 期：2023年第76卷第8期

页      面：1817-1832页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0837[工学-安全科学与工程] 0802[工学-机械工程] 

基　　金：supported by the Healthcare AI Convergence R&D Program through the National IT Industry Promotion Agency of Korea(NIPA)funded by the Ministry of Science and ICT(No.S0102-23-1007) the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2017R1A6A1A03015496) 

主　　题：Facial emotion recognition landmark-based feature extraction ensemble network robustness to the changes in illumination and background dangerous situation detection accident prevention 

摘      要：Emotion recognition based on facial expressions is one of the most critical elements of human-machine *** conventional methods for emotion recognition using facial expressions use the entire facial image to extract features and then recognize specific emotions through a pre-trained *** contrast,this paper proposes a novel feature vector extraction method using the Euclidean distance between the landmarks changing their positions according to facial expressions,especially around the eyes,eyebrows,nose,***,we apply a newclassifier using an ensemble network to increase emotion recognition *** emotion recognition performance was compared with the conventional algorithms using public *** results indicated that the proposed method achieved higher accuracy than the traditional based on facial expressions for emotion *** particular,our experiments with the FER2013 database show that our proposed method is robust to lighting conditions and backgrounds,with an average of 25% higher performance than previous ***,the proposed method is expected to recognize facial expressions,especially fear and anger,to help prevent severe accidents by detecting security-related or dangerous actions in advance.