A Reliability-Based Multimodal Framework for 3D Face Recognition under Occlusion

Abstract

Face recognition in real-world and uncontrolled environments is greatly affected when the face is partially covered by masks, glasses, scarves, hair, or due to pose-related self-occlusion. Although three-dimensional (3D) face recognition is generally more robust to lighting changes and moderate pose variations, its performance still reduces when important facial regions are heavily covered. To overcome this problem, this study presents an occlusion-aware hybrid biometric framework for reliable 3D face recognition. The proposed method combines reconstructed 3D shape features, deep texture features, and additional biometric cues using an adaptive weighted fusion approach. An occlusion detection and generative reconstruction module is used to recover missing facial regions before feature extraction, and an attention mechanism reduces the impact of unreliable areas while focusing on important facial features. Extensive experiments on benchmark datasets, such as Bosphorus, BU-3DFE, and FRGC v2.0, show that the proposed framework performs better than strong single-modality and traditional multimodal methods. The proposed system reaches an accuracy of up to 98.7%, even in partial occlusions, and significantly reduces the Equal Error Rate (EER), demonstrating its effectiveness and suitability for real-world biometric authentication applications.