Analysing the performance of Viola-Jones and multi-task convolution neural networks face detection algorithms using real-time video sequences

In recent years, face detection has been a hot research area in computer vision, serving as the first step in both face recognition and facial expression detection. However, several challenges exist when detecting faces in real-time, including pose variations, varying lighting conditions, and partial occlusions on face and video images. Despite the existence of numerous face detection algorithms, this study focuses on evaluating the Viola-Jones and multi-task convolutional neural network (MTCNN) algorithms, which have been widely used for face detection in several research studies. The objective of this comparative study is to analyse these two widely used face detection algorithms in the context of the aforementioned challenges using real-time video sequences and benchmark datasets. For this study, video sequences were collected from the LIRIS children spontaneous facial expression video database, and a real-time video dataset was captured in the centre for machine learning and intelligence laboratory. The results show that MTCNN achieved an average true positive rate accuracy of 94.33%, whereas the Viola-Jones algorithm achieved 73.33% accuracy when conducting experiments with various face detection challenge scenarios.