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High Performance and Efficient Real-Time Face Detector on Central Processing Unit Based on Convolutional Neural Network

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Abstract
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.
Author(s)
푸트로 무하마드 드위스난토락소노 쿠니안고로조강현
Issued Date
2021
Type
Article
Keyword
central processing unit (CPU)Computer architectureConvolutionConvolutional neural network (CNN)Detectorsface detectorFace recognitionFacesFeature extractionlight architecturereal timeReal-time systems
DOI
10.1109/TII.2020.3022501
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9133
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_ieee_primary_9187678&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,High%20Performance%20and%20Efficient%20Real-Time%20Face%20Detector%20on%20Central%20Processing%20Unit%20Based%20on%20Convolutional%20Neural%20Network&offset=0&pcAvailability=true
Publisher
IEEE Transactions on Industrial Informatics
Location
미국
Language
영어
ISSN
1551-3203
Citation Volume
17
Citation Number
7
Citation Start Page
4449
Citation End Page
4457
Appears in Collections:
Engineering > IT Convergence
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