Cloud-Enabled Facial Emotion Recognition: A Comprehensive AWS Rekognition Analysis
Yogendra Chowdary, Batta Chaitany A Kishore, Sai Harsha Pingali, Chalavadi Kvss Pavan Aditya, K. V. D. Kiran · 2024
This research introduces a serverless cloud-based a system for identifying facial emotions that is highly accurate and scalable, but not too complex. It uses A WS services like Lambda, S3 and Rekognition to simplify deployment and increase scalability--and make it available anywhere. Depending on a serverless architecture that is implemented by using cloud infrastructure, the system makes an API for real- time emotion analysis of facial images accessible. Bypassing intricate models, the system focuses on optimal image preprocessing techniques for efficient feature extraction. Performance evaluation rigorously assesses the system's accuracy, speed, and scalability across diverse metrics. The results affirm the system's efficiency and precision in a serverless cloud-based environment. With its innovative approach, this project demonstrates the power of serverless cloud computing for facial emotion recognition. Beyond its technical capabilities, the system has a wide range of possible uses that could affect sentiment analysis, personalized user experiences, and human -computer interaction.