Robust Face Recognition Using Deep Learning and Ensemble Classification

Facial recognition systems are widely used in various applications such as security, healthcare, and authentication but face significant challenges in uncontrolled environments. Poor lighting conditions can obscure facial features, introduce shadows, and distort spatial relationships, while pose changes are critical for accurate identification. Existing methods often struggle to balance accuracy, computational efficiency, and robustness. Deep learning has become popular for automatically learning features through convolution layers. This study proposes a robust framework that integrates contrast-limited adaptive histogram equalization (CLAHE) and adaptive gamma correction for illumination normalization and multi-task cascaded convolutional networks (MTCNN) for precise face detection under varying poses and lighting conditions. This study proposes a deep learning-based approach for face recognition using multiple models, including VGG16, VGG19, ResNet50, ResNet101, and MobileNetV2. For classification, an ensemble of SVM, XGBoost, and random forest classifiers is combined using weighted averaging. The approach is tested on datasets like CASIA3D and 105Pinsface, which include variations in illumination conditions. Using deep learning for automated hierarchical feature extraction and ensemble strategies, experimental results demonstrate significant improvements in recognition accuracy and enhanced robustness against lighting and pose variations while ensuring scalability for real-world applications. The approach achieved 99.91% accuracy on the CASIA3D dataset and 98.77% on the 105PinsFace dataset, showcasing its effectiveness across challenging conditions.