Developed an advanced ensemble learning system combining Transformers and Convolutional Neural Networks for masked face recognition, achieving 94.22% accuracy through innovative stacking techniques. The project enhanced existing methodologies by implementing optimized weight distribution and stacked generalization, demonstrating significant improvements over state-of-the-art approaches. Notable achievements included a 2.2% accuracy gain over traditional ensemble methods and robust performance across multiple datasets.