Multi-scale Face Detection Based on Single Neural Network

Hongzhe LIU; Shaopeng YANG; Jiazheng YUAN; Xueqiao WANG; Jianming XUE

doi:10.11999/JEIT180163

Volume 40 Issue 11

Oct. 2018

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Hongzhe LIU, Shaopeng YANG, Jiazheng YUAN, Xueqiao WANG, Jianming XUE. Multi-scale Face Detection Based on Single Neural Network[J]. Journal of Electronics & Information Technology, 2018, 40(11): 2598-2605. doi: 10.11999/JEIT180163

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Multi-scale Face Detection Based on Single Neural Network

doi: 10.11999/JEIT180163

1.
Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China
2.
Beijing Open University, Beijing 100081, China
3.
Institute of Computer Technology, Beijing Union University, Beijing 100101, China

Funds: The National Natural Science Foundation of China (61571045), The Supporting Plan for Cultivating High Level Teachers in Colleges and Universities in Beijing (IDHT20170511), The National Science and Technology Support Project (2015BAH55F03), The Foundation of Beijing Union University (Zk10201703), The Foundation of Beijing Municipal Education Commission (KM201811417002)

Received Date: 2018-02-07
Rev Recd Date: 2018-07-05

Available Online: 2018-07-23

Publish Date: 2018-11-01

Abstract

Abstract

Face detection is finding and locating all faces in the input image, and then returning the position and size of the faces. It is an important direction of target detection. In order to solve the problem which is caused by the diversity of face size, a new single shot multiscale face algorithm is presented based on feature fusion. This method combines predictions from multiple feature maps with different resolutions to handle faces of various sizes, and the fusion of the feature maps in the shallow layers can improve the detection accuracy of the small size face by introducing the contextual information. Experimental results on the FDDB and WIDERFACE datasets confirm that the proposed method has competitive accuracy. Additionally, the object proposal step is removed, which makes the method fast. The proposed model achieves 87.9%, 93.2% and 93.4% Mean Average Precision (MAP) on the WIDERFACE sub-datasets respectively, at 35 fps. The proposed method outperforms a comparable state-of-the-art HR model, and at the same time improves the speed while ensuring the accuracy.
- Multi-scale face detection,
- Contextual information,
- Feature map fusion,
- Convolution neural network

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References(26)

References

JIANG Huaizu and LEARNED M E. Face detection with the faster r-cnn[C]. IEEE International Conference on Automatic Face & Gesture Recognition, Washington, D.C., USA, 2017: 650–657.

YANG Shuo, LUO Ping, LOY C, et al. WIDERFACE: A face detection benchmark[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 5525–5533.

CROSSWHITE N, BYRNE J, STAUFFER C, et al. Template adaptation for face verification and identification[C]. IEEE International Conference on Automatic Face & Gesture Recognition, Washington, D.C., USA, 2017: 1–8.

MAJUMDAR A, SINGH R, and VATSA M. Face verification via class sparsity based supervised encoding[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1273–1280 doi: 10.1109/TPAMI.2016.2569436

GAO Yuan, MA Jiayi, and YUILLE A L. Semi-supervised sparse representation based classification for face recognition with insufficient labeled samples[J]. IEEE Transactions on Image Processing, 2017, 26(5): 2545–2560 doi: 10.1109/TIP.2017.2675341

HARIS KHAN M, MCDONAGH J, and TZIMIROPOULOS G. Synergy between face alignment and tracking via discriminative global consensus optimization[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hawaii, USA, 2017: 3791–3799.

GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 580–587.

VIOLA P and JONES M. Rapid object detection using a boosted cascade of simple features[C]. IEEE Computer Society Conference on Computer Vision & Pattern Recognition, Kauai, USA, 2001: 511.

LI Jianguo, WANG Tao, and ZHANG Yimin. Face detection using SURF cascade[C]. IEEE International Conference on Computer Vision Workshops, Ontario, Canada, 2012: 2183–2190.

MATHIAS M, BENENSON R, PEDERSOLI M, et al. Face detection without bells and whistles[C]. European Conference on Computer Vision, Zurich, Switzerland, 2014: 720–735.

LI Haoxiang, LIN Zhe, SHEN Xiaohui, et al. A convolutional neural network cascade for face detection[C]. Computer Vision and Pattern Recognition. Boston, USA, 2015: 5325–5334.

WU Shuzhe, KAN M, SHAN Shiguang, et al. Funnel-structured cascade for multi-view face detection with alignment-awareness[J]. Neurocomputing, 2016, 221(C): 138–145.

YANG Shuo, LUO Ping, CHEN C L, et al. Faceness-Net: Face detection through deep facial part responses[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(8): 1845–1859 doi: 10.1109/TPAMI.2017.2738644

GIRSHICK R. Fast r-cnn[C]. Proceedings of The IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 1440–1448.

REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137–1149 doi: 10.1109/TPAMI.2016.2577031

LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: Single shot multibox detector[C]. European Conference on Computer Vision, Amsterdam, Netherlands, 2016: 21–37.

DAI Jifeng, LI Yi, HE Kaiming, et al. R-fcn: Object detection via region based fully convolutional networks[C]. Advances in Neural Information Processing Systems, Barcelona, Spain, 2016: 379–387.

ZHU Chenchen, ZHENG Yutong, LUU K, et al. CMS-RCNN: Contextual multi-scale region-based CNN for unconstrained face detection[OL]. arXiv preprint arXiv:1606.05413, 2016.

HU Peiyun and RAMANAN D. Finding tiny faces[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii, USA, 2017: 1522–1530.

ERHAN D, SZEGEDY C, TOSHEV A, et al. Scalable object detection using deep neural networks[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 2147–2154.

CHEN Chenyi, LIU Mingyu, TUZEL O, et al. R-cnn for small object detection[C]. Asian Conference on Computer Vision, Taipei, China, 2016: 214–230.

BELL S, LAWRENCE ZITNICK C, BALA K, et al. Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 2874–2883.

WONG R Y and HALL E L. Sequential hierarchical scene matching[J]. IEEE Transactions on Computers, 1978, 27(4): 359–366 doi: 10.1109/TC.1978.1675108

FU C Y, LIU Wei, RANGA A, et al. DSSD: Deconvolutional single shot detector[OL]. arXiv preprint arXiv:1701.06659, 2017.

WEI Xiang, ZHANG Dongqing, YU H, et al. Context-aware single-shot detector[C]. IEEE Winter Conference on Applications of Computer Vision, Lake Tahoe, USA, 2018: 1784–1793.

HOWARD A G. Some improvements on deep convolutional neural network based image classification[OL]. arXiv preprint arXiv:1312.5402, 2013.

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