Face Detection in Real Time Live Video Using Yolo Algorithm Based on Vgg16 Convolutional Neural Network
Htet Aung, A.V. Bobkov, Nyan Lin Tun
Abstract
Face detection is not only one of the most studied topics in the computer vision field but also a very important task in many applications, such as security access control systems, video surveillance, human-computer interface, and image database management. Nowadays, various methods were developed for face detection systems like Viola-Jones, RCNN, SSD, and so on. Many researchers are still trying to improve face detection systems with various illustrations, poses, skin colors, and real-time detection. This paper intends to combine YOLO (You Only Look Once) algorithm with the VGG16 pre-trained convolutional neural network to propose an improvement for face detection systems. Experimental results show that proposed method has detected the test image set with over 95 % of average precision. Also, our proposed method considerably increased face detection speed in real-time live video. The experiment of this work was using the Image Processing Toolbox and the Deep Learning Toolbox in MATLAB.