Capstone 02: Face Recognition System

Project Goal

Build an end-to-end face recognition pipeline:

• Synthetic face dataset (gaussian blobs as face embeddings)
• ArcFace-style margin loss training
• FAISS embedding index for fast nearest-neighbor search
• Cosine similarity matching with configurable threshold
• Full evaluation: FAR vs FRR tradeoff curve

Architecture

Enrollment Phase:
  Face Image → CNN Encoder → 512-dim L2-normalized embedding → FAISS Index

Inference Phase:
  Query Image → CNN Encoder → Query Embedding
      → FAISS search (top-k nearest neighbors)
      → Cosine similarity threshold decision
      → MATCH / NO MATCH

Key Concepts

ArcFace Margin Loss

Standard softmax loss treats all wrong classes equally. ArcFace adds an angular margin $m$ in the embedding space: $$L = -\log \frac{e^{s(\cos(\theta_{y_i} + m))}}{e^{s(\cos(\theta_{y_i} + m))} + \sum_{j \neq y_i} e^{s \cos \theta_j}}$$

This forces embeddings of the same identity to cluster tightly, and different identities to have large angular separation.

FAISS Index Selection

Metrics to Report

• TAR@FAR=0.01%: True Accept Rate when False Accept Rate = 0.01%
• EER: Equal Error Rate (FAR = FRR)
• AUC: Area under the ROC curve
• Top-1 Accuracy: correct match at rank 1