CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Real-Time Multi-Scale Face Detector on Embedded Devices
Zhao, Xu1,2; Liang, Xiaoqing1,2; Zhao, Chaoyang1,2; Tang, Ming1,2; Wang, Jinqiao1,2
Source PublicationSENSORS
ISSN1424-8220
2019-05-01
Volume19Issue:9Pages:22
Corresponding AuthorZhao, Chaoyang(chaoyang.zhao@nlpr.ia.ac.cn)
AbstractFace detection is the basic step in video face analysis and has been studied for many years. However, achieving real-time performance on computation-resource-limited embedded devices still remains an open challenge. To address this problem, in this paper we propose a face detector, EagleEye, which shows a good trade-off between high accuracy and fast speed on the popular embedded device with low computation power (e.g., the Raspberry Pi 3b+). The EagleEye is designed to have low floating-point operations per second (FLOPS) as well as enough capacity, and its accuracy is further improved without adding too much FLOPS. Specifically, we design five strategies for building efficient face detectors with a good balance of accuracy and running speed. The first two strategies help to build a detector with low computation complexity and enough capacity. We use convolution factorization to change traditional convolutions into more sparse depth-wise convolutions to save computation costs and we use successive downsampling convolutions at the beginning of the face detection network. The latter three strategies significantly improve the accuracy of the light-weight detector without adding too much computation costs. We design an efficient context module to utilize context information to benefit the face detection. We also adopt information preserving activation function to increase the network capacity. Finally, we use focal loss to further improve the accuracy by handling the class imbalance problem better. Experiments show that the EagleEye outperforms the other face detectors with the same order of computation costs, on both runtime efficiency and accuracy.
Keywordface detection ARM-based devices model acceleration computer vision
DOI10.3390/s19092158
Indexed BySCI
Language英语
Funding ProjectNatural Science Foundation of China[61772527] ; Natural Science Foundation of China[61806200] ; Natural Science Foundation of China[61876086]
Funding OrganizationNatural Science Foundation of China
WOS Research AreaChemistry ; Electrochemistry ; Instruments & Instrumentation
WOS SubjectChemistry, Analytical ; Electrochemistry ; Instruments & Instrumentation
WOS IDWOS:000469766800202
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23713
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorZhao, Chaoyang
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhao, Xu,Liang, Xiaoqing,Zhao, Chaoyang,et al. Real-Time Multi-Scale Face Detector on Embedded Devices[J]. SENSORS,2019,19(9):22.
APA Zhao, Xu,Liang, Xiaoqing,Zhao, Chaoyang,Tang, Ming,&Wang, Jinqiao.(2019).Real-Time Multi-Scale Face Detector on Embedded Devices.SENSORS,19(9),22.
MLA Zhao, Xu,et al."Real-Time Multi-Scale Face Detector on Embedded Devices".SENSORS 19.9(2019):22.
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