Effective Machine Learning Schemes for Face Recognition in Higher-Dimensional Datasets

Abstract

Face recognition systems play a vital role in personal identification, particularly in security applications. However, high-dimensional image data often reduces the effectiveness of machine learning (ML) models by increasing computational complexity and training difficulty. This study aims to improve face recognition performance by combining dimensionality reduction and deep feature extraction techniques. Two benchmark datasets, Filtered LFW and a custom VGGFace2 dataset, were used to evaluate two ML-based models. The first model integrates Linear Discriminant Analysis (LDA), CNN (ResNet18), and K-Nearest Neighbors (KNN), while the second combines CNN (ResNet18) with Naïve Bayes (NB). Both models were trained, tested, and evaluated using accuracy, precision, recall, F1-score, and execution time. On the LFW dataset, the CNN-LDA-KNN model achieved the best performance, with 97.82% accuracy, 0.9799 precision, 0.9782 recall, 0.9782 F1-score, and 68.85 seconds evaluation time, outperforming the CNN-NB model (96.71% accuracy). Similarly, on the VGGFace2 dataset, CNN-LDA-KNN obtained 93.46% accuracy compared with 78.87% for CNN-NB. These findings demonstrate that integrating LDA with CNN-based feature extraction and KNN classification significantly enhances face recognition performance on high-dimensional image datasets while maintaining competitive computational efficiency.