Facial Recognition System Using 3D Morphable Models

Abstract

Facial recognition in unconstrained environments remains challenging due to large pose variation, non-uniform illumination, partial occlusion, and expression dynamics. This manuscript presents a full-stack facial recognition system centered on 3D Morphable Models (3DMMs) to canonicalize facial geometry and appearance before identity embedding and matching. We formulate the 3DMM with separate identity and expression subspaces and estimate per-subject shape, texture, camera, and illumination via a robust differentiable fitting pipeline that combines photometric, landmark, and regularization losses with occlusion-aware weighting. After fitting, we generate pose- and light-normalized canonical representations—UV texture maps and neutralized meshes—that feed a margin-based deep embedding network trained for identity discrimination.

A score-level fusion of 3D geometric similarity and 2D appearance embeddings yields improved robustness under extreme head rotations (±60°), directional lighting, and synthetic occlusions. A comprehensive statistical analysis reports verification True Accept Rate at 1% False Accept Rate (TAR@FAR=1%), Equal Error Rate (EER), and Rank-1 identification accuracy with 95% confidence intervals derived by stratified bootstrap; significance against a strong 2D baseline is measured via McNemar’s test. In simulated experiments on a multi-pose, multi-illumination benchmark (≈2,000 identities, ≈10,000 probe images), the proposed 3DMM-based pipeline improves Rank-1 by 3.6–5.8 percentage points, halves EER, and raises TAR@FAR=1% particularly for profile views and occluded faces. We discuss system design, ablations, runtime considerations, limitations (ageing, heavy occlusion >40%, cross-sensor shift), and ethical concerns, and outline future extensions including self-supervised 3D pretraining and photorealistic data generation for long-tail conditions.