Phase I: Architecture Design and SetupThis phase establishes the core infrastructure for scalability and security. 1.1. Backend Infrastructure Setup (B1, B5, B9)Select and provision cloud resources (e.g., AWS, GCP, Azure). Set up the API gateway with load balancing. Define the /v1/liveness/verify endpoint. Set up object storage (e.g., S3, GCS) with mandatory AES-256 encryption and restrict access via Role-Based Access Control (RBAC). Deploy and configure the chosen Message Queuing System (e.g., Kafka or SQS) to serve as the processing queue. Design the database schema for audit logs, session data, and the Liveness Result API. 1.2. Development Environment and ComplianceEstablish TLS/HTTPS across all services. Define initial data retention policies (e.g., GDPR compliant) and implement consent tracking mechanisms. Provision GPU-accelerated instances for the ML processing fleet. Install and configure the dedicated ML serving framework (e.g., NVIDIA Triton).Phase II: Core Backend Pipeline DevelopmentThis phase implements the data flow, security, and asynchronous processing logic. 2.1. API Ingestion and Security (B2, B11) Develop the high-volume REST endpoint. Implement server-side handling for multipart upload/chunking for reliable file transfer. Implement comprehensive server-side checks for file type, size, and metadata (Input Validation). The API must execute the Asynchronous Processing Trigger: on valid upload, save the file to storage and place a job ID into the message queue, immediately returning an HTTP 202 Accepted status to the client. 2.2. Processing Worker Fleet (B6, B8)Develop the worker application that monitors the message queue. Implement the logic for the worker to pull the job ID from the queue, retrieve the video file from encrypted storage, and initiate the processing sequence. Implement detailed, step-by-step logging within the worker service to track the lifecycle of each liveness check (Audit Logging). 2.3. Client Notification Service (B4)Develop the dedicated internal API (Liveness Result API) to store and retrieve the final processing outcome. Implement either the Polling endpoint (client asks for status) or the Webhook service (server pushes result to client).Phase III: ML Model Integration and Liveness LogicThis phase focuses on embedding the intelligence into the processing workers. 3.1. Video Pre-processing and Quality Check (M2, M3)Integrate the FFmpeg library into the worker to handle video decoding and frame extraction. Implement the Quality Threshold Enforcement logic: detect face presence and calculate Inter-Ocular Distance (IOD). If IOD is below threshold ($\ge 80$ pixels), the job fails instantly as "Low Quality." 3.2. Liveness Core Feature Extraction (M5)Develop or integrate libraries to calculate the advanced features required for Passive Liveness: Optical Flow to calculate motion vectors, PPG/Heartbeat Detection (if applicable), and preparation of frame crops for Reflection/Texture Analysis. 3.3. Model Execution and Validation (M1, M4, M6)Send the pre-processed frames/features to the deployed High-Performance Serving instance. If the input is an Active Liveness video, run the Facial Landmark Tracking module to verify the correct sequence of head movements. Retrieve the raw confidence score from the ML model and apply the defined security threshold to produce the final binary decision (REAL/SPOOF). Log the raw score. Phase IV: Testing, Security Audit, and DeploymentThis final phase ensures the system is ready for production and complies with regulations.Integration Testing (E2E): Test the entire pipeline from client upload through the queue, ML processing, to the final status notification.Performance and Load Testing: Stress-test the API Gateway and the Scalable Worker Fleet to ensure the target latency is met under peak load.Security Penetration Testing: Conduct a third-party security audit focusing on data transmission and storage access.Compliance Audit: Verify that the automated Data Retention Compliance mechanisms are correctly implemented and tested for scheduled deletion.Deployment: Final deployment to the production environment and transition to the MLOps monitoring phase.