I’m proud to have designed and implemented a Face Recognition Authentication System using Python and DeepFace AI, deployed with a cloud-native and automation approach on GCP.
The backend is a FastAPI-based Python service running on Cloud Run, where face embeddings are generated and validated using DeepFace AI. These embeddings are securely stored and retrieved from Google Firestore, providing a fully managed, scalable, and cost-efficient data layer.
The frontend runs on a Google Compute Engine VM, handling real-time webcam capture, validations, loaders, and secure communication with the backend services.
The entire system is containerized using Docker and deployed through a fully automated CI/CD pipeline using GitHub, Cloud Build, and Artifact Registry, enabling seamless builds and deployments with minimal manual effort.
This project reflects my strong interest and hands-on experience in AI integration, backend system design, cloud deployment, CI/CD automation, and cost optimization using serverless and managed GCP services.
🎥 The attached video demonstrates the complete end-to-end flow — from face capture to authentication and automated deployment.
hashtag#Python hashtag#DeepFace hashtag#AI
hashtag#FastAPI hashtag#CloudRun hashtag#Firestore
hashtag#Docker hashtag#CloudBuild hashtag#CICD
hashtag#GCP hashtag#CloudEngineering hashtag#VM INSTANCE
hashtag#Automation hashtag#DevOps hashtag#CostOptimization
Top comments (1)
Really impressive project! From a user’s perspective, it’s great to see how AI, security, and cloud services come together so smoothly. The end-to-end flow from face capture to authentication and automated deployment shows strong practical thinking, not just theory. The GCP and CI/CD setup makes it feel very production-ready.