An Improved Convolutional Neural Network (Icnn) Model And U-Net Segmentation Of Facial Features For 2d-3d Facial Image Analysis
DOI:
https://doi.org/10.29284/ijasis.12.1s.2026.1-23Keywords:
Face recognition, two-dimensional (2D) facial images, three-dimensional (3D), preprocessing, Segmentation, Euclidean and Geodesic distances and Improved Convolutional Neural Network (ICNN) classifier.Abstract
Face recognition is a pivotal biometric tool extensively utilized in commercial and law enforcement applications for identifying individuals. This study introduces a novel framework for face recognition that leverages two-dimensional (2D) facial images sourced from diverse origins to construct three-dimensional (3D) face meshes using 468 MediaPipe landmarks. The preprocessing pipeline incorporates adaptive median filtering to enhance image quality, followed by deep learning-based segmentation via U-Net architectures to effectively separate the foreground (face) from the background, thereby reducing noise and improving detection precision. Feature vectors are generated by calculating Euclidean and Geodesic distances and their ratios between identified landmarks, capturing intricate facial geometries. These feature vectors are subsequently trained using an Improved Convolutional Neural Network (ICNN) classifier, designed to maintain high accuracy in environments characterized by significant pose variations, facial expressions, and occlusions. According to the experimental results, a range of image attributes may be learned by the improved convolutional neural network approach proposed in this paper, increasing the network's ability to identify images and extract information efficiently.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
