Human–Computer Interaction (HCI) is an intelligent way, which aims at creating scalable and flexible solutions. Large corporations and technological firms support HCI because it enables them to benefit from infrastructure and technology that is available on demand for information-centric applications without requiring the usage of public clouds. Facial expression recognition and software-based facial expression recognition systems are essential due to their ability to mimic human coding abilities. This study provides two approaches for estimating age and gender characteristics from human faces and suggests a system for identifying the emotional state of individuals given facial expressions. This study aims to further understand how human age and gender affect face expressions. Currently, the model can distinguish between seven different emotions based on a person’s facial data: anger, disgust, happiness, fear, sadness, surprise, and neutral. The proposed system is divided into three segments: (a) Gender Detection. (b) Age Detection. (c) Emotion Recognition. Additionally, we have made use of pre-trained models (Transfer Learning) and the designs of various deep learning models, like CNN. The model performance in terms of the Recognition system’s accuracy is displayed by the real-time evaluation metrics.


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    Titel :

    Deep Learning-Based Facial Expression Recognition System for Age, Gender, and Emotions


    Weitere Titelangaben:

    Smart Innovation, Systems and Technologies


    Beteiligte:
    Jha, Pradeep Kumar (Herausgeber:in) / Jamwal, Prashant (Herausgeber:in) / Tripathi, Brajesh (Herausgeber:in) / Garg, Deepak (Herausgeber:in) / Sharma, Harish (Herausgeber:in) / Sheeba Joice, C. (Autor:in) / Hitha Shanthini, S. (Autor:in) / Chandru, R. (Autor:in) / Devadharshan, T. (Autor:in) / Eswar, D. (Autor:in)

    Kongress:

    Congress on Control, Robotics, and Mechatronics ; 2024 ; Warangal, India February 03, 2024 - February 04, 2024



    Erscheinungsdatum :

    14.11.2024


    Format / Umfang :

    13 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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