SERVERLESS IMAGE RECOGNITION WITH AWS REKOGNITION AND LAMBDA

Abstract: The rapid proliferation of visual data in the digital era has necessitated advanced, scalable, and cost-effective solutions for automated image analysis. Traditional server-bound architectures often struggle with the unpredictable nature of image processing workloads, leading to either resource underutilization or high latency during peak traffic. This paper presents an end-to-end serverless architecture designed to perform real-time image recognition and metadata extraction using Amazon Rekognition and AWS Lambda. By leveraging the Function-as-a-Service (FaaS) model, the system eliminates the need for manual server provisioning, patching, and scaling, providing a robust framework that responds dynamically to incoming data streams.

At the core of this system is an event-driven workflow initiated by the ingestion of visual assets into Amazon S3. The upload event triggers an AWS Lambda function that invokes the Amazon Rekognition API — a deep-learning-based service capable of identifying objects, scenes, text, and facial attributes without requiring specialized machine learning expertise. Results comprising detected labels and confidence scores are persisted in Amazon DynamoDB, enabling massive parallelization that can handle thousands of simultaneous uploads with minimal latency. The system also supports facial analysis, celebrity recognition, and content moderation using a pay-as-you-go pricing model.

Keywords: Serverless Computing, AWS Lambda, Amazon Rekognition, Image Recognition, Amazon S3, DynamoDB, Cloud Computing, FaaS, Event-Driven Architecture, Deep Learning