A One-Dimensional Deep Learning Model for Face Recognition Using 3D Facial Landmark Features
Received: 2 July 2025 | Revised: 10 August 2025 and 26 August 2025 | Accepted: 6 September 2025 | Online: 9 October 2025
Corresponding author: Duaa J. Al Hammami
Abstract
Deep learning-based face recognition systems often rely on high-resolution images and computationally expensive 2D Convolutional Neural Networks (CNNs), making them unsuitable for real-time or edge-device deployment. To address this limitation, this study proposes a lightweight one-dimensional (1D) deep learning model for face recognition using 3D facial landmark features extracted via MediaPipe Face Mesh. The normalized 3D coordinates of 148 discriminative facial landmarks are formatted as 1D sequences and fed into a hybrid 1D CNN–LSTM architecture that captures both local spatial patterns and global structural dependencies. The model achieves 100% classification accuracy on two publicly available datasets—MUCT and FaceScrub—while significantly reducing computational overhead. These results demonstrate that landmark-based 1D deep learning models offer a highly accurate, efficient, and scalable solution for face recognition in resource-constrained environments.
Keywords:
face recognition, deep learning, facial landmarks, 1D convolutional neural network, biometric authentication, feature extractionDownloads
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