期刊
APPLIED SCIENCES-BASEL
卷 9, 期 20, 页码 -出版社
MDPI
DOI: 10.3390/app9204344
关键词
face detection; tiny faces; pre-identification mechanism; cascaded detector; deep learning; convolutional neural network
类别
资金
- National Natural Science Foundation of China [61762061, 61963027, 61703198]
- Natural Science Foundation of Jiangxi Province, China [20161ACB20004]
- Natural Science Foundation for Distinguished Young Scholars of Jiangxi Province [2018ACB21014]
- Jiangxi Key Laboratory of Smart City [20192BCD40002]
Although the face detection problem has been studied for decades, searching tiny faces in the whole image is still a challenging task, especially in low-resolution images. Traditional face detection methods are based on hand-crafted features, but the features of tiny faces are different from those of normal-sized faces, and thus the detection robustness cannot be guaranteed. In order to alleviate the problem in existing methods, we propose a pre-identification mechanism and a cascaded detector (PMCD) for tiny-face detection. This pre-identification mechanism can greatly reduce background and other irrelevant information. The cascade detector is designed with two stages of deep convolutional neural network (CNN) to detect tiny faces in a coarse-to-fine manner, i.e., the face-area candidates are pre-identified as region of interest (RoI) based on a real-time pedestrian detector and the pre-identification mechanism, the set of RoI candidates is the input of the second sub-network instead of the whole image. Benefiting from the above mechanism, the second sub-network is designed as a shallow network which can keep high accuracy and real-time performance. The accuracy of PMCD is at least 4% higher than the other state-of-the-art methods on detecting tiny faces, while keeping real-time performance.
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