Face Matching in 5 Lines of Python: Detect, Encode, Compare

Your fintech app needs KYC. A user uploads a selfie and a photo of their ID. Your backend needs to answer one question: is this the same person? Between the shutter click and the green checkmark, three things happen. This article breaks down those three steps, then shows how to implement the whole pipeline in 5 lines of Python. The 3 Steps of Face Matching Every face matching system follows the same pipeline: Step 1: Detect Find the face in the image. The detector outputs a bounding box around each face. Detector Accuracy (WIDER FACE) Speed Best for RetinaFace 96.9% ~3.8s/face (CPU) Max accuracy, batch MTCNN 84.8% ~0.4s/face Good balance MediaPipe Lower ~0.04s/face Real-time, mobile YuNet Good ~0.03s/face Fastest on CPU Step 2: Encode Convert the face into a numerical vector (embedding). Two photos of the same person produce similar vectors. Photo of Alice → [0.23, -0.45, 0.78, 0.12, ...] (128 numbers) Photo of Alice → [0.21, -0.47, 0.76, 0.14, ...] (similar!) Photo of Bob → [-0.67, 0.