Smart Mirror with Emotion Monitoring
Vaishnavi Choudhari 1, Shruti Gumgaonkar2, Bhagyashree Thakre3, Sakshi Dhirde4, Arya Warudkar5
Mrs. N. K. Warambhe6
1,2,3,4,5Department of Electronics & Telecommunication Engg, RTMNU, Priyadarshini J.L. College Of Engg. Nandanvan, Nagpur, Maharashtra, India
6Asst.Prof.,Department of Electronics & Telecommunication Engg, RTMNU, Priyadarshini J.L. College Of Engg.
Nandanvan, Nagpur, Maharashtra, India
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ABSTRACT- In recent years, the integration of artificial intelligence has revolutionized human-computer interaction by enabling devices to provide personalized user experiences. This study focuses on the development of a Smart Mirror with Emotion Monitoring, which utilizes advanced facial recognition and deep learning algorithms to detect and classify user emotions in real time. By analyzing facial expressions, the system identifies emotional states such as happiness, sadness, surprise, angry, and neutrality. Based on these detected emotions, the mirror responds dynamically by displaying personalized content, including motivational messages, and aiming to enhance mental well-being. Beyond emotional responsiveness, the smart mirror also functions as a daily assistant, offering real-time information such as weather updates, calendar, and emotion monitoring insights. Its applications extend beyond residential use to sectors such as healthcare, retail, and corporate environments, where emotion-based interactions can improve user engagement and experience. In healthcare, for instance, the mirror can assist individuals dealing with stress or mental health conditions by providing therapeutic interactions. Similarly, in commercial settings, the mirror can be integrated into customer service environments to enhance user satisfaction. The proposed system is built using state-of-the-art computer vision techniques, deep learning models, and IoT integration to ensure accurate emotion detection and seamless functionality. A robust dataset is employed to train and optimize the facial recognition model, ensuring high accuracy in emotion classification. To validate the system’s performance, extensive experiments are conducted, evaluating parameters such as detection accuracy, response time, and user satisfaction. The results demonstrate the system’s effectiveness in providing real-time emotional insights and personalized interactions, contributing to the advancement of intelligent assistive technologies. By incorporating emotional intelligence into smart mirror technology, this study bridges the gap between functionality and user-centric design, paving the way for next-generation AI-driven smart devices
