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

#computervision #python #ai #tutorial

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, 0.89, -0.12, 0.55, ...]  (different)

Model	LFW Accuracy	Embedding Size
FaceNet (Google, 2015)	99.63%	128
ArcFace (2018)	99.83%	512
SFace (2021)	99.60%	128

Step 3: Compare

Measure the distance between two embeddings.

Metric	How it works	Match threshold
Cosine similarity	Angle between vectors. 1.0 = identical	> 0.6
Euclidean distance	Straight-line distance. Lower = closer	< 1.1

The Code (5 Lines)

import requests

response = requests.post(
    "https://faceanalyzer-ai.p.rapidapi.com/compare-faces",
    headers={"x-rapidapi-key": "YOUR_API_KEY", "x-rapidapi-host": "faceanalyzer-ai.p.rapidapi.com"},
    files={"source_image": open("selfie.jpg", "rb"), "target_image": open("id_photo.jpg", "rb")},
)
print(response.json()["body"])