کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6941793 | 870621 | 2016 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
FaceHunter: A multi-task convolutional neural network based face detector
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
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چکیده انگلیسی
In this paper, we propose a new multi-task Convolutional Neural Network (CNN) based face detector, which is named FaceHunter for simplicity. The main idea is to make the face detector achieve a high detection accuracy and obtain much reliable face boxes. Reliable face boxes output will be much helpful for further face image analysis. To reach this goal, we design a deep CNN network with a multi-task loss, i.e., one is for discriminating face and non-face, and another is for face box regression. An adaptive pooling layer is added before full connection to make the network adaptive to variable candidate proposals, and the truncated SVD is applied to compress the parameters of the fully connected layers. To further speed up the detector, the convolutional feature map is directly used to generate the candidate proposals by using Region Proposal Network (RPN). The proposed FaceHunter is evaluated on the AFW dataset, FDDB dataset and Pascal Faces respectively, and extensive experiments demonstrate its powerful performance against several state-of-the-art detectors.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing: Image Communication - Volume 47, September 2016, Pages 476-481
Journal: Signal Processing: Image Communication - Volume 47, September 2016, Pages 476-481
نویسندگان
Dong Wang, Jing Yang, Jiankang Deng, Qingshan Liu,