Diffusion for De-Occlusion: Accessory-Aware Diffusion Inpainting for Robust Ear Biometric Recognition

TL;DR

This paper introduces a diffusion-based ear inpainting method to remove accessory occlusions, enhancing the robustness of transformer-based ear biometric recognition in unconstrained environments.

Contribution

The study proposes a novel accessory-aware diffusion inpainting technique specifically designed for ear images to improve biometric recognition accuracy under occlusion conditions.

Findings

Inpainting improves recognition accuracy in occluded ear images.

Diffusion-based method preserves key ear structures effectively.

Pre-processing with inpainting enhances transformer-based recognition performance.

Abstract

Ear occlusions (arising from the presence of ear accessories such as earrings and earphones) can negatively impact performance in ear-based biometric recognition systems, especially in unconstrained imaging circumstances. In this study, we assess the effectiveness of a diffusion-based ear inpainting technique as a pre-processing aid to mitigate the issues of ear accessory occlusions in transformer-based ear recognition systems. Given an input ear image and an automatically derived accessory mask, the inpainting model reconstructs clean and anatomically plausible ear regions by synthesizing missing pixels while preserving local geometric coherence along key ear structures, including the helix, antihelix, concha, and lobule. We evaluate the effectiveness of this pre-processing aid in transformer-based recognition systems for several vision transformer models and different patch sizes for a range of benchmark datasets. Experiments show that diffusion-based inpainting can be a useful pre-processing aid to alleviate ear accessory occlusions to improve overall recognition performance.