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http://dx.doi.org/10.6109/jkiice.2020.24.1.50

Research and Optimization of Face Detection Algorithm Based on MTCNN Model in Complex Environment  

Fu, Yumei (Department of Computer Engineering, Dong-Eui University)
Kim, Minyoung (Research Institute of ICT Fusion and Convergence, Dong-Eui University)
Jang, Jong-wook (Department of Computer Engineering, Dong-Eui University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.1, 2020 , pp. 50-56 More about this Journal
Abstract
With the rapid development of deep neural network theory and application research, the effect of face detection has been improved. However, due to the complexity of deep neural network calculation and the high complexity of the detection environment, how to detect face quickly and accurately becomes the main problem. This paper is based on the relatively simple model of the MTCNN model, using FDDB (Face Detection Dataset and Benchmark Homepage), LFW (Field Label Face) and FaceScrub public datasets as training samples. At the same time of sorting out and introducing MTCNN(Multi-Task Cascaded Convolutional Neural Network) model, it explores how to improve training speed and Increase performance at the same time. In this paper, the dynamic image pyramid technology is used to replace the traditional image pyramid technology to segment samples, and OHEM (the online hard example mine) function in MTCNN model is deleted in training, so as to improve the training speed.
Keywords
Face Detection; Feature extraction; Image Preprocessing; MTCNN; OHEM;
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