A comparitive study of MTCNN, Viola-Jones, SSD and YOLO face detection algorithms
Sumit Tariyal, Rahul Chauhan, Yogesh Bijalwan, Ruchira Rawat, Rupesh Gupta
Abstract
A crucial part of computer vision, face detection has many uses, such as security systems and facial recognition. The four well-known face detection algorithms Viola-Jones, MTCNN, SSD, and YOLO are compared in this abstract with an emphasis on how well they balance speed and accuracy. Viola-Jones is regarded as a pioneer in the field and is known for its strong accuracy, it might not be as quick as more modern algorithms. MTCNN, a facial detection specialist, is very accurate, especially when it comes to identifying facial landmarks, but it might not always be able to keep up with the demands of real-time speed. SSD is appropriate for a variety of real-time face detection applications because it achieves a remarkable balance between speed and accuracy. Renowned for its real-time performance, YOLO provides competitive speed and accuracy, which makes it the best option for environments with limited resources and high dynamism. This analysis highlights the benefits and drawbacks of each algorithm, giving practitioners and researchers the knowledge they need to choose the best option for their particular face detection tasks. Face detection technology is expected to continue growing because of the dynamic nature of this field, which guarantees continuous advancements in both speed and accuracy.