Comparative Analysis of Face Detection Models: MTCNN, YOLO, and a Hybrid Approach

Face detection is a vital subroutine in many computer vision systems, such as facial recognition systems, emotion detection systems and surveillance systems. Two of the many such algorithms that have emerged powerful in terms of accuracy as well as computation efficiency are Multi-task Cascaded Convolutional Networks (MTCNN) and You Only Look Once (YOLO). This paper involves a comparative review of MTCNN, YOLO and a Hybrid Model that fuses the two methods. The models are trained on the fareselmenshawii/face-detection-dataset and the performance is measured by accuracy, running time and the stability of detecting the faces. The experimental findings show that there are apparent trade-offs that exist between speed and detection accuracy across the models, but the hybrid strategy shows a balanced execution, as it efficiently takes this advantage of the strengths of both MTCNN and YOLO.