Cloud

Developed a hybrid cloud application utilizing advanced face-recognition techniques with Python. The system processes classroom videos, identifies students’ faces, and retrieves academic records from DynamoDB. Integrated AWS and OpenStack, enabling seamless scalability and cost-effectiveness by utilizing both public and private cloud resources. An extension of Project 2, this approach enhances control and flexibility in triggering AWS Lambda functions through OpenStack VMs.

Developed a cloud-based application using Python’s face-recognition library to integrate advanced face-recognition techniques. The system processes classroom videos, identifies students’ faces, and retrieves their academic records from DynamoDB. Implemented on AWS Lambda PaaS for efficient, accurate, and cost-effective student recognition and academic record management.

The service will take in images in the PNG format from users, use the deep learning model to perform image recognition, and return the top prediction from the model to the user as plain text. The importance of this service is to provide a scalable and efficient solution for image recognition that can be accessed remotely by users, without the need for them to train their own models or have access to specialized hardware.