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Real-Time Multi-Scale Face Detector on Embedded Devices
Zhao X(赵旭)1,2; Liang XQ(梁孝庆)1,2; Zhao CY(赵朝阳)1,2; Tang M(唐明)1,2; Wang JQ(王金桥)1,2
Source PublicationSensors

Face 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.

Indexed BySCI
WOS IDWOS:000469766800202
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Document Type期刊论文
Corresponding AuthorZhao CY(赵朝阳)
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhao X,Liang XQ,Zhao CY,et al. Real-Time Multi-Scale Face Detector on Embedded Devices[J]. Sensors,2019,2019(9):2158.
APA Zhao X,Liang XQ,Zhao CY,Tang M,&Wang JQ.(2019).Real-Time Multi-Scale Face Detector on Embedded Devices.Sensors,2019(9),2158.
MLA Zhao X,et al."Real-Time Multi-Scale Face Detector on Embedded Devices".Sensors 2019.9(2019):2158.
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