Face Detection in Real Time Live Video Using Yolo Algorithm Based on Vgg16 Convolutional Neural Network
Htet Aung, Alexander V. Bobkov, Nyan Linn Tun · 2021
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.