Traditional computer vision architectures often make a critical mistake: attempting to execute content moderation and biometric identification within a single synchronous pipeline. In the FindWay architecture, we implemented strict Fault Domain Isolation to decouple these responsibilities. At the outer perimeter, a lightweight CNN moderator (EfficientNet-B0) acts as the "Data Sentinel". Its sole purpose is to intercept non-compliant content within milliseconds at the Active Job layer, preventing malicious or invalid binary streams from reaching core persistence layers.
Only media that successfully clears this initial boundary moves to the secondary, isolated biometric core powered by ArcFace. The image is projected into a 512-dimensional vector embedding capturing pure geometric feature ratios. Rather than storing sensitive raw biometric images, the local PostgreSQL instance indexes these non-invertible vectors using pgvector with HNSW graph structures. This setup enables near-instant vector similarity lookups via cosine distance (latency < 5 ms), strictly enforces privacy compliance, and guarantees that deep learning pipeline bottlenecks never compromise main application availability.
English
Traditional computer vision architectures often make a critical mistake: attempting to execute content moderation and biometric identification within a single synchronous pipeline. In the FindWay architecture, we implemented strict Fault Domain Isolation to decouple these responsibilities. At the outer perimeter, a lightweight CNN moderator (EfficientNet-B0) acts as the "Data Sentinel". Its sole purpose is to intercept non-compliant content within milliseconds at the Active Job layer, preventing malicious or invalid binary streams from reaching core persistence layers.
Only media that successfully clears this initial boundary moves to the secondary, isolated biometric core powered by ArcFace. The image is projected into a 512-dimensional vector embedding capturing pure geometric feature ratios. Rather than storing sensitive raw biometric images, the local PostgreSQL instance indexes these non-invertible vectors using pgvector with HNSW graph structures. This setup enables near-instant vector similarity lookups via cosine distance (latency < 5 ms), strictly enforces privacy compliance, and guarantees that deep learning pipeline bottlenecks never compromise main application availability.