Leveraging MongoDB in Real-Time Emotion Recognition from Video for Scalable and Efficient Data Handling

Real-time emotion recognition from video poses significant challenges in handling large-scale and continuously growing datasets. Traditional relational databases often fail to meet the scalability and efficiency requirements of such applications. MongoDB, a NoSQL database, offers significant advantages in scalability, speed, and data management, making it an ideal choice for video-based emotion recognition systems. This paper explores the use of MongoDB to optimize the management of video data in real-time emotion recognition, leveraging its features like sharding, indexing, and replication. We demonstrate how MongoDB's advanced features enhance the performance and reliability of emotion recognition systems by reducing latency and processing time. Through experimental results, we show that MongoDB outperforms traditional relational databases and other NoSQL solutions in handling large datasets efficiently. Future work will explore integrating MongoDB with cloud platforms to improve scalability and incorporate advanced deep learning algorithms for better emotion recognition accuracy.