Machine Learning - Fingerprint Recognition (E2E)

Overview

Two approaches: classical minutiae extraction + matching or deep CNN embeddings + nearest neighbor matching with quality checks.

Deep embeddings (outline)

# Preprocess (denoise/normalize), feed to CNN for embedding, then compare
# emb_a, emb_b: L2-normalized vectors
import numpy as np
sim = float((emb_a * emb_b).sum())  # cosine similarity for normalized vectors
is_match = sim >= 0.75