Incorporating MTCNN and DeepFace: A Novel Hybrid Deep Learning Method for Human Face Detection and Recognition (International Journal of Engineering and Computer Science, 2023)
The realm of face detection has become a focal point of extensive research, driven by its diverse applications spanning computer vision, communication, and automatic control systems. Realizing real-time recognition of multiple faces within embedded systems poses a formidable challenge due to the intricate computational demands involved. This challenge necessitates a deep exploration of facets such as face detection, expression recognition, face tracking, and pose estimation. Accurately identifying a face from a single image stands as the core challenge, primarily due to the non-rigid nature of faces, resulting in variations in size, shape, color, and more. Furthermore, the complexity of face detection amplifies when confronted with unclear images, occlusions, suboptimal lighting conditions, off-angle poses, and various other factors. This study presents an innovative framework for multiple face recognition. Through extensive experiments, the system's prowess in simultaneously recognizing up to 10 different human face poses in real time was showcased, achieving remarkable processing speeds as low as 0.21 seconds. The system demonstrated an impressive minimum recognition rate of 93.15%, underscoring the effectiveness of the proposed methodology. While the primary emphasis lies on frontal human faces, the system is adept at handling poses beyond the frontal orientation, marking a significant advancement in the domain of face detection and recognition.