Comparison of the Parametrically Optimized Implementation of Viola–Jones Object Detection Method and MTCNN

Abstract

Face detection on images is a classical computer vision task. Solution to this problem is used in a wide range of apps, mainly in those connected with providing for the elements of administration (access to the data based on a person's face) and elements of control (perimeter safety control systems). The most spread object's face detecting methods may be divided in two groups: classical one (common example is Viola–Jones method) and one based on a neural network (common example is a cascaded convolutional neural network). There are plenty comparisons of face detection methods that, however, do not usually consider the potential for optimization within the parameters of realization methods in question. This paper focuses on the comparison of the parametrically optimized ways of implementation of methods which are typical for each class. Optimization is being carried out in accordance with the algorithm suggested in this paper. It reveals that the optimized Viola–Jones method is inferior to MTCNN by 20–50 % in view of the quality metrics but outruns it 7–14 times when it comes to the processing speed per image. It shows that without using algorithm optimization by parameters, it is barely impossible to get a fair quality and capacity just as the average value of the quality and capacity metrics are visibly lower than the optimal ones.