一种多层特征融合的人脸检测方法
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摘要
由于姿态、光照、尺度等原因,卷积神经网络需要学习出具有强判别力的特征才能应对复杂场景下的人脸检测问题。受卷积神经网络中特定特征层感受野大小限制,单独一层的特征无法应对多姿态多尺度的人脸,为此提出了串联不同大小感受野的多层特征融合方法用于检测多元化的人脸;同时,通过引入加权降低得分的方法,改进了目前常用的非极大值抑制算法,用于处理由于遮挡造成的相邻人脸的漏检问题。在FDDB和WiderFace两个数据集上的实验结果显示,文中提出的多层特征融合方法能显著提升检测结果,改进后的非极大值抑制算法能够提升相邻人脸之间的检测准确率。
To address the issues of pose, lighting variation, and scales, convolutional neural networks(CNNs) need to learn features with strong discrimination handle the face detection problem in complex scenes. Owing to the size limitations of the specific feature layer's receptive field in convolutional neural networks, the features computed from a single layer of the CNNs are incapable of dealing with faces in multi poses and multi scales. Therefore, a multi-layer feature fusion method that is realized by fusing the different sizes of receptive fields is proposed to detect diversified faces.Moreover, via introducing the method of weighted score decrease, the present usual non-maximum suppression algorithm was improved to deal with the detection omission of neighboring faces caused by shielding. The experiment results with the FDDB and Wider Face datasets demonstrated that the fusion method proposed in this study can significantly boost detection performance, while the improved non-maximum suppression algorithm can increase the detection accuracy between neighboring faces.
To address the issues of pose, lighting variation, and scales, convolutional neural networks(CNNs) need to learn features with strong discrimination handle the face detection problem in complex scenes. Owing to the size limitations of the specific feature layer's receptive field in convolutional neural networks, the features computed from a single layer of the CNNs are incapable of dealing with faces in multi poses and multi scales. Therefore, a multi-layer feature fusion method that is realized by fusing the different sizes of receptive fields is proposed to detect diversified faces.Moreover, via introducing the method of weighted score decrease, the present usual non-maximum suppression algorithm was improved to deal with the detection omission of neighboring faces caused by shielding. The experiment results with the FDDB and Wider Face datasets demonstrated that the fusion method proposed in this study can significantly boost detection performance, while the improved non-maximum suppression algorithm can increase the detection accuracy between neighboring faces.
引文
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[2]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,OH,USA,2014:580–587.
[3]GIRSHICK R.Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision.Santiago,Chile,2015:1440–1448.
[4]REN Shaoqing,HE Kaiming,GIRSHICK R,et al.Faster RCNN:towards real-time object detection with region proposal networks[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems.Montreal,Canada,2015,1:91–99.
[5]HUANG Lichao,YANG Yi,DENG Yafeng,et al.DenseBox:unifying landmark localization with end to end object detection[J].ar Xiv preprint ar Xiv:1509.04874,2015.
[6]YU Jiahui,JIANG Yuning,WANG Zhangyang,et al.UnitBox:An advanced object detection network[C]//Proceedings of the 2016 ACM on Multimedia Conference.Amsterdam,The Netherlands,2016:516–520.
[7]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[C]//Proceedings of the International Conference on Learning Representations.Oxford,USA,2015.
[8]LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Boston,MA,USA,2015:3431–3440.
[9]VIOLA P,JONES M.Rapid object detection using a boosted cascade of simple features[C]//Proceedings of the 2001IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Kauai,HI,USA,2001,1:I-511–I-518.
[10]LOWE D G.Distinctive image features from scale-invariant keypoints[J].International journal of computer vision,2004,60(2):91–110.
[11]DALAL N,TRIGGS B.Histograms of oriented gradients for human detection[C]//Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.San Diego,CA,USA,2005,1:886–893.
[12]OSUNA E,FREUND R,GIROSIT F.Training support vector machines:an application to face detection[C]//Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.San Juan,Argentina,1997:130–136.
[13]FRIEDMAN J,HASTIE T,TIBSHIRANI R.Additive logistic regression:a statistical view of boosting(with discussion and a rejoinder by the authors)[J].The annals of statistics,2000,29(5):337–407.
[14]ZITNICK C L,DOLLáR P.Edge boxes:locating object proposals from edges[C]//Proceedings of the 13th European Conference on Computer Vision.Zurich,Switzerland,2014:391–405.
[15]UIJLINGS J R R,VAN DE SANDE K E A,GEVERS T,et al.Selective search for object recognition[J].International journal of computer vision,2013,104(2):154–171.
[16]LI Haoxiang,LIN Zhe,SHEN Xiaohui,et al.A convolutional neural network cascade for face detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Boston,MA,USA,2015:5325–5334.
[17]FARFADE S S,SABERIAN M J,LI Lijia.Multi-view face detection using deep convolutional neural networks[C]//Proceedings of the 5th ACM on International Conference on Multimedia Retrieval.Shanghai,China,2015:643–650.
[18]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Image Net classification with deep convolutional neural networks[C]//Proceedings of the 26th Annual Conference on Neural Information Processing Systems 2012.Lake Tahoe,Nevada,USA,2012:1097–1105.
[19]HE Kaiming,ZHANG Xiangyu,REN Shaoqing,et al.Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas,NV,USA,2016:770–778.
[20]HARIHARAN B,ARBELáEZ P,GIRSHICK R,et al.Hypercolumns for object segmentation and fine-grained localization[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Boston,MA,USA,2015:447–456.
[21]BODLA N,SINGH B,CHELLAPPA R,et al.Improving object detection with one line of code[J].ar Xiv preprint ar Xiv:1704.04503,2017.
[22]YANG Shuo,LUO Ping,LOY C C,et al.Wider Face:A face detection benchmark[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas,NV,USA,2016:5525–5533.
[23]JAIN V,LEARNED-MILLER E.FDDB:A benchmark for face detection in unconstrained settings[R].UMass Amherst Technical Report UMCS-2010-009,2010.
[24]DENG Jia,DONG Wei,SOCHER R,et al.Image Net:A large-scale hierarchical image database[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Miami,FL,USA,2009:248–255.
[25]KINGMA D P,BA J L.Adam:A method for stochastic optimization[C]//Proceedings of International Conference on Learning Representations.Toronto,Canada,2015.
[26]YANG Bin,YAN Junjie,LEI Zhen,et al.Aggregate channel features for multi-view face detection[C]//Proceedings of the 2014 IEEE International Joint Conference on Biometrics(IJCB).Clearwater,FL,USA,2014:1–8.
[27]MARKUS N,FRLJAK M,PANDZIC I S,et al.A method for object detection based on pixel intensity comparisons organized in decision trees[J].Co RR,2014.
[28]MATHIAS M,BENENSON R,PEDERSOLI M,et al.Face detection without bells and whistles[C]//Proceedings of the 13th European Conference on Computer Vision.Zurich,Switzerland,2014:720–735.
[29]CHEN Dong,REN Shaoqing,WEI Yichen,et al.Joint cascade face detection and alignment[C]//Proceedings of the 13 th European Conference on Computer Vision.Zurich,Switzerland,2014:109–122.
[30]OHN-BAR E,TRIVEDI M M.To boost or not to boost?On the limits of boosted trees for object detection[C]//Proceedings of the 23rd International Conference on Pattern Recognition(ICPR).Cancun,Mexico,2016:3350–3355.
[31]YANG Shuo,LUO Ping,LOY C C,et al.From facial parts responses to face detection:A deep learning approach[C]//Proceedings of the IEEE International Conference on Computer Vision.Santiago,Chile,2015:3676–3684.