Intelligent Monitoring System Using Facial Expression

Intelligent Monitoring System Using Facial Expression

1st Moin Hasan
Department of CSE
JAIN(Deemed-to-be-University)
Bengaluru, India.

4th Rohit Raj
Department of CSE
JAIN (Deemed-to-be-University)
Bengaluru, India.
21btrcs060@jainuniversity.ac.in

2nd Ritik Raj
Department of CSE
JAIN (Deemed-to-be-University)
Bengaluru, India. 21btrcs059@jainuniversity.ac.in

5th Ashutosh Kr
Department of CSE
JAIN (Deemed-to-be-University)
Bengaluru, India.
21btrcs011@jainuniversity.ac.in

3rd Rahul Ranjan Kr
Department of CSE
JAIN (Deemed-to-be-University)
Bengaluru, India.
21btrcs053@jainuniversity.ac.in

6th Ansh Vajpai
Department of CSE
JAIN (Deemed-to-be-University)
Bengaluru, India.
21btrcs009@jainuniversity.ac.in

Abstract— Facial Emotion Recognition (FER) has emerged as a significant component in the creation of emotionally intelligent systems, attempting to bridge the communication gap between humans and technology. This project presents a real-time face emotion detection web application that uses live camera feeds to determine users' emotional states and dynamically improves user engagement according to mood. To provide a smooth and responsive experience, the system makes use of the DeepFace framework, OpenCV for video processing, and Flask for backend administration. When the application detects an emotion, such as happiness, sorrow, anger, surprise, fear, or neutrality, it instantly modifies the web interface's background color to match the user's mood and recommends a carefully chosen playlist of YouTube songs that are appropriate for the emotion. The user experience can be further customized with optional features like facial recognition and predicted age detection. The suggested method provides a dynamic and immersive platform with real-time input, thereby addressing the shortcomings of conventional static emotion analysis systems. Through the integration of visual, aural, and interactive components, the program improves emotional engagement and shows how emotion-aware services may be used in a variety of industries, including customer service, entertainment, mental wellness, and adaptive learning. Because of the system's emphasis on accessibility, simplicity, and computational economy, it can function properly even on common consumer hardware without the need for expensive

GPUs. The potential for developing sympathetic human-computer interfaces that react to users' current states both rationally and emotionally is demonstrated by this work. Embedding direct multimedia playing, enabling multi-emotion detection per frame, and expanding its application to mobile and edge computing platforms are possible future advances.

Keywords— Facial Emotion Detection, DeepFace, OpenCV, Flask, Real-Time Processing, Mood-Based Adaptation, Human-Computer Interaction