Detailed Work Report: February 20, 2026**

1. Core Architectural Shift

• Infrastructure Design: Initiated the transition to a dedicated FastAPI backend for the validation service, moving away from a single-script prototype.

• Compute Optimization: Performed a “Sanity Check” on the compute requirements, deciding to proceed with CPU-based inference rather than TPU. MediaPipe, ONNX, and CLIP embeddings were determined to run efficiently on standard CPU architectures for this stage.

• Endpoint Planning: Defined the structure for four primary production endpoints: validate, capture, analyse, and admin.

2. Validation Engine Development (Validator Class)

• Advanced Landmarks Integration: Integrated the MediaPipe Face Landmarker (face_landmarks_detector.task) to utilize a 468-point facial mesh for high-precision geometry analysis.

• Geometric Algorithm Implementation:

• Eye Aspect Ratio (EAR): Implemented the EAR formula to detect eye state (open/closed) with a standard threshold of 0.2.

• Smile Detection: Developed a geometry-based smile detector using a mouth-to-face width ratio with a defined threshold of 0.4.

• Accessory Detection:

• Hat/Headgear: Loaded an ONNX neural network (hat_beard_model.onnx) for headwear detection, implementing confidence scoring with a 40% threshold.

• Glasses: Integrated the GlassesClassifier for automated eyewear detection.

3. Production Readiness & Logic Optimization

• Early Return Optimization: Refined the validation pipeline to use a “Fail-Fast” strategy. The service now rejects invalid images (e.g., non-square aspect ratios or multiple faces) immediately to save computational resources.

• Model Loading Pattern: Established a “Load-Once” singleton pattern, ensuring all ML models are loaded into memory during the application’s startup phase rather than per request, drastically reducing latency.

• Structured Error Responses: Designed a production-grade response schema that returns specific failure codes (e.g., MULTIPLE_FACES, NOT_SQUARE) and detailed metrics instead of simple booleans.

4. Project Standardization

• Naming: Rebranded the validation microservice as FaceGate API (or Anana Validate Engine) to reflect its role as a strict gatekeeper for the platform.

• Directory Structure: Defined a scalable, professional folder structure (app/core/, app/api/, app/services/) to maintain modularity between routing, business logic, and ML model management.

Project Status: The validation logic is fully audited and optimized for CPU deployment on Railway, with all key biometric thresholds calibrated for production use.

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Day 16 - February 20, 2026

Project: FaceGate API / Anana Validate Engine

Status: Validation logic fully audited and optimized for CPU deployment on Railway.

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1. Core Architectural Shift

• Transitioned to a dedicated FastAPI backend for the validation service, moving away from a single-script prototype.
• Optimized compute requirements, confirming CPU-based inference is sufficient for MediaPipe, ONNX, and CLIP embeddings.
• Planned four primary production endpoints: validate, capture, analyse, and admin.

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2. Validation Engine Development

• Integrated MediaPipe Face Landmarker (face_landmarks_detector.task) for high-precision 468-point facial mesh analysis.
• Implemented geometric algorithms:
  • Eye Aspect Ratio (EAR) for eye state detection (threshold: 0.2).
  • Smile detection using mouth-to-face width ratio (threshold: 0.4).
• Accessory detection:
  • ONNX neural network (hat_beard_model.onnx) for headwear detection (confidence threshold: 40%).
  • GlassesClassifier for automated eyewear detection.

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3. Production Readiness & Logic Optimization

• Refined validation pipeline with a Fail-Fast strategy, immediately rejecting invalid images to save resources.
• Established a Load-Once singleton pattern for model loading, reducing latency by loading all ML models at startup.
• Designed structured error responses with specific failure codes (e.g., MULTIPLE_FACES, NOT_SQUARE) and detailed metrics.

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4. Project Standardization

• Rebranded the microservice as FaceGate API (Anana Validate Engine) to reflect its gatekeeper role.
• Defined a scalable folder structure (app/core/, app/api/, app/services/) for modularity between routing, business logic, and ML model management.

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Summary: Validation logic is fully audited, optimized for CPU deployment, and all key biometric thresholds are calibrated for production use.