Hi, I'mLe Huy Hoang

Since the COVID-19 pandemic, wearing masks has become a common practice worldwide, further compounded by the growing issue of air pollution. As masks have become an essential part of daily life, they pose a significant challenge to facial recognition systems, which struggle to accurately identify individuals when key facial features are obscured. This study focuses on improving the performance of existing masked facial recognition systems to better adapt to these real-world conditions. This work builds on an existing framework for masked face detection, proposing several optimizations to enhance recognition accuracy. The approach integrates Mediapipe for reliable face detection, combined with ResNet50 for feature extraction and a novel Feature Concatenation technique that merges multiple facial feature vectors to create more robust representations. To improve model adaptability to masked faces, we employ data augmentation techniques that simulate various real-world conditions, such as different mask types and lighting variations. Additionally, we performed ResNet50 fine-tuning on a specific masked dataset to increase robustness against partial face occlusions. Recognition is then carried out using a cosine similarity-based distance metric, ensuring both computational efficiency and high accuracy. Our optimizations resulted in a significant performance improvement, achieving a precision of 99.47% on the Celebrity dataset. These results demonstrate the potential to refine existing methodologies for better masked facial recognition, contributing to more reliable applications in security, healthcare, and other sectors.

Facial recognition systems often face challenges in balancing accuracy and computational efficiency, particularly in real-world environments with varying lighting conditions and image quality. Many existing methods rely on individual machine learning models, which may not fully capture the complex patterns needed for high-precision recognition across diverse datasets. This research addresses these limitations by implementing and comparing several machine learning algorithms—K-Nearest Neighbors (KNN), Random Forest, and Support Vector Machine (SVM)—and improving their performance through ensemble techniques such as Voting and Stacking. The process includes key steps like image preprocessing, facial detection using 'hog' and 'cnn' models, and feature extraction with facial encodings and Local Binary Pattern (LBP). Additionally, extensive data augmentation techniques—including image rotation, noise injection, LBP, and brightness adjustment—were applied to simulate real-world variations and improve model robustness. Dimensionality reduction with principal component analysis(PCA) and hyperparameter tuning via GridSearchCV further optimized performance. Our proposed method achieved a facial recognition accuracy of 99.22%, exceeding the results of the SVM 95.34% [15 ]. This demonstrates the effectiveness of integrating machine learning models and applying ensemble methods, leading to more reliable facial recognition systems and marking a significant improvement over current approaches.