FinTech Onboarding

Overview

This is the landing page of the UI. Allow camera access to continue the onboarding.

A lightweight KYC (Know Your Customer) prototype for face-based identity verification. This service compares a live face captured via webcam with a face extracted from an uploaded ID document.

It uses YuNet for face detection and SFace for face recognition and embedding extraction, both from opencv_zoo.

The system is containerized using Docker and consists of the following services:

• Web frontend: User interface for live face capture via webcam and uploading documents

• Backend: Performs face detection and face recognition for comparison (see Model Notes section)

• BFF (Backend For Frontend): Mediates access to backend infrastructure

AMD Solution Blueprints are packaged as Helm charts for deployment on a Kubernetes cluster. For development or further exploration, the source code is public and available in the Solution Blueprints GitHub repository.

Architecture

Component	Role
BFF (Backend For Frontend)	FastAPI proxy alongside the frontend; mediates calls to the backend
Backend	Face detection and similarity comparison using YuNet and SFace
VLM	Vision–language model (default Mistral Small 3.2 24B)

⚠️ Restrictions: The backend uses YuNet (face detection) and SFace (face recognition) models, which are deployed inside the Backend. The models are downloaded on first startup, which may cause slower initial launch times.

Model Notes

The service uses YuNet (face detection) and SFace (face recognition) from opencv_zoo. Both models are licensed for commercial use (MIT and Apache 2.0).

⚠️ Known restrictions:

• SFace achieves competitive accuracy on standard benchmarks (LFW ~99.55%).

• Robustness to occlusions (glasses, medical masks, etc.) is not formally documented for SFace; community reports indicate a drop in similarity scores under strong facial occlusions, especially around the eyes and lower face.

Key Features

• Live face capture: Browser webcam flow with a guided liveness-style check before any match step

• Two-sided ID upload: Front image with face and back image with barcode, aimed at US driver’s license–style documents for the demo workflow

• Visual models (CV + VLM): OpenCV Zoo YuNet and SFace handle face detection/recognition

• End-to-end flow: Guided steps from live face capture through document processing to a final similarity score that compares the live face with the face on the uploaded document.

Getting Started

This is a quick start guide on how to deploy the blueprint. For advanced options, such as reusing an existing AIM, providing a Hugging Face token, or overriding storage classes, see Deploying Solution Blueprints with Helm or explore the advanced deployment guide.

This blueprint supports AMD Instinct (default) and AMD Radeon platforms. The section below covers the default Instinct deployment. For Radeon deployment and other advanced options, see:

• Deploy on AMD Instinct

• Deploy on AMD Radeon

Prerequisites

System Requirements

The blueprint requires the following cluster resources by default:

Resource	Default Configuration
GPUs	2
CPUs	4 CPU cores
RAM	64 Gi

To deploy to the Kubernetes cluster, ensure the following prerequisites are met:

• kubectl: Installed and configured to communicate with the cluster

• Helm 3.17 or higher: Installed on your local machine

Deployment

Solution Blueprints are packaged as OCI-compliant Helm charts in the Docker Hub registry and can be deployed to a Kubernetes cluster with a single command. Define the name (deployment name) and the namespace (Kubernetes namespace), then pipe the output of helm template to kubectl apply -f -:

name="my-deployment"
namespace="my-namespace"
helm template $name oci://registry-1.docker.io/amdenterpriseai/aimsb-fintech-onboarding \
  | kubectl apply -f - -n $namespace

Note: You can create a namespace using kubectl create namespace $namespace.

To check the status of the deployment, run:

kubectl get pods -n $namespace

Wait until all pods report Running and Ready.

Connect to UI

To connect to the UI, port-forward to 8080. The UI will then be available at http://localhost:8080 in your browser.

kubectl port-forward services/${name}-aimsb-fintech-onboarding-ui 8080:8080 -n $namespace

How to Use the Service

1. Allow camera access when prompted by the browser.

2. Live face capture: Click the Start Liveness Check button and follow the instructions.

3. If you don’t have a US driver’s license, you can create a test version of one. Use this guide. Please note: The test driver licenses you create should be used only for testing this service. Do not use them for any other purpose or in production environments.

4. Upload front side (photo with face): Select a photo of your document that includes a face (currently supports driver’s licenses).

5. Upload back side (barcode): Select a photo of your document that includes a barcode (currently supports driver’s licenses).

6. Click the Process Documents button and review how the data extracted from the front and back of your document compares.

7. Click the Run match button to see the similarity score between your live face and the face on the uploaded document.

Clean Up

When you are finished, remove the deployed resources:

helm template "$name" oci://registry-1.docker.io/amdenterpriseai/aimsb-fintech-onboarding | kubectl delete -f - -n "$namespace"

Third-Party Components

This Solution Blueprint uses multiple third-party components. To see the full set of software and Python dependencies, explore the repository source and dependency files. The table below lists key components only. For further license information, refer to each component’s official documentation.

Component	License
FastAPI	MIT
OpenCV	Apache 2.0
OpenCV Zoo (YuNet, SFace)	See the repositories

Terms of Use

AMD Solution Blueprints are released under the MIT License, which governs the parts of the software and materials created by AMD. Third-party Software and Materials used within the Solution Blueprints are governed by their respective licenses.