As industries transition toward fully digital onboarding and KYC verification, securing identity checks has become a high priority. Simple comparisons of static photographs are often vulnerable to spoofing, creating a need for more interactive verification methods. This face verification system matches a user's static ID card photo against a real-time, 10-second video recording, ensuring a far more robust authentication check during registration.
Inside the Detection and Embeddings Pipeline
The processing engine runs on a modular python-based backend powered by FastAPI. First, Multi-task Cascaded Convolutional Networks (MTCNN) are used to isolate facial boundaries from both the ID photo and the recording frames, while MediaPipe provides real-time guidance prompts to help users align their faces. Once isolated, the model extracts high-dimensional feature vectors using DeepFace with the Facenet512 architecture, calculating distance metrics between the ID image and the video frames to confirm identity.
User Interface and Current Performance
To ensure a smooth user experience, the system utilizes a clean, responsive client interface designed with React 18, Next.js, and Tailwind CSS. The frontend manages the webcam stream, dynamically filtering and selecting high-quality frames during the 10-second recording window to optimize matching accuracy. Currently, the system operates with an overall face detection accuracy of 65% to 80% and a verification speed of 5 to 10 seconds per request, offering an efficient baseline for secure, biometric-free identity verification.
