10th International Congress on Information and Communication Technology

Authors - Rayner Henry Pailus, Rayner Alfred
Abstract - Pose face recognition systems often struggle with the variability of illumination and face poses, especially when images are captured in uncontrolled environments. This paper addresses these challenges by proposing a novel face recognition approach: Multiple Adaptive Derivative Face Recognition (MADFR). Our method focuses on optimizing face recognition at every processing level to enhance overall accuracy. By incorporating multiple illumination training samples and diverse training data, including both controlled and wild images, our approach improves the robustness of face recognition models. Our analysis highlights the limitations of existing models like FaceNet, particularly in handling images with multiple face poses and varying background illuminations. We propose pose estimation landmarking and localization with multiple landmarks, which significantly enhances discriminant features. The effectiveness of our approach is demonstrated through extensive experiments on three datasets: LFW, Pointing 04, and Carl Dataset. Our results show that the proposed MADFR system, combined with the ensemble method MADBOOST, consistently outperforms other models. Specifically, MFRF 10 emerged as the top-performing model across all datasets, exhibiting high accuracy and low error rates. This research makes a significant contribution to the eld of face recognition by providing a robust solution that effectively handles the complexities of real-world scenarios. In conclusion, the MADFR system, with its optimized processing and decision-making capabilities, demonstrates substantial improvements in face recognition accuracy, paving the way for more reliable and effective face recognition technologies.