Enhancing Facial Recognition Through 3D Depth Map Optimization

Abstract

Facial recognition systems have traditionally relied on 2D imagery, but they remain vulnerable to challenges such as pose variation and lighting inconsistencies. To address these limitations, this paper presents a novel pipeline that converts 3D facial models into 2D depth maps, thereby enhancing recognition accuracy. Using Open3D for rendering and OpenCV for image preprocessing, we evaluate a range of techniques—including Gaussian blur, bilateral filtering, and Sobel edge detection—to optimize feature extraction. Experiments conducted on the BU-3DFE dataset and a custom dataset reveal that the combination of bilateral filtering and Sobel edge detection yields an 8–12% improvement in recognition accuracy, while maintaining computational efficiency (~1.7 seconds per model). These findings highlight the value of depth map preprocessing in biometric recognition systems and pave the way for future integration with deep learning architectures.