期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 17, 期 7, 页码 4449-4457出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2020.3022501
关键词
Feature extraction; Face recognition; Detectors; Real-time systems; Convolution; Computer architecture; Faces; Convolutional neural network (CNN); central processing unit (CPU); face detector; real time; light architecture
类别
资金
- Research Fund of University of Ulsan
Face detection is crucial for the development of face recognition, expression, tracking, and classification. While conventional methods have accuracy constraints on challenging conditions, CNN methods show high performances, though requiring expensive hardware. The proposed CNN-based face detector achieves state-of-the-art performance on benchmark datasets and runs efficiently on a CPU.
Face detection is crucial in the development of face recognition, expression, tracking, and classification. Conventional methods have accuracy constraints on several challenging conditions, including nonfrontal faces, occlusions, and complex backgrounds. However, the convolutional neural network (CNN) methods produce high performances despite a large amount of computation. Therefore, CNN requires expensive hardware and is not suitable for low-cost central processing units (CPUs). This article develops a light architecture for a CNN-based real-time face detector. The proposed architecture consists of two main modules, the backbone to extract distinctive facial features and multilevel detection to perform prediction at multiple scales. Furthermore, it utilizes several approaches to enhance the training result, including balancing loss and tweaks on the training configuration. The proposed detector has one stage and is trained using the input of images from WIDER FACE with challenges, which contains more challenging images than other datasets. As a result, the detector achieves state-of-the-art performance on several benchmark datasets compared with the other CPU-based models. Then, its efficiency is superior to that of competitors, as it runs at 53 frames per second on a CPU for video graphics array resolution images.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据