Image Remapping Data Augmentation Approach for Improving Fisheye Face Recognition

Fisheye cameras present a challenge for face recognition due to their wide-angle perspective and image distortion. This paper introduces a novel approach to enhancing training data for fisheye-based face recognition without requiring image calibration. We employ five image-remapping transformations to diversify and expand the training dataset and evaluate the effectiveness of this approach using deep learning networks: HRNetV2 and ResNet50. The results demonstrate significant improvements in classification accuracy when utilizing authentic fisheye facial data. Specifically, HRNetV2 exhibits an increase of 30.2%, while ResNet50’s performance improves by 11.8% compared to their respective baseline performances. This study presents a fresh method for refining face recognition in fisheye camera scenarios, thereby extending its potential for real-world applications.