浏览全部资源
扫码关注微信
收稿：2018-04-12，
修回：2018-8-31，
纸质出版：2019-02-16
移动端阅览
目的
2
传统人脸检测方法因人脸多姿态变化和人脸面部特征不完整等问题，导致检测效果不佳。为解决上述问题，提出一种两层级联卷积神经网络（TC_CNN）人脸检测方法。
方法
2
首先，构建两层卷积神经网络模型，利用前端卷积神经网络模型对人脸图像进行特征粗略提取，再利用最大值池化方法对粗提取得到的人脸特征进行降维操作，输出多个疑似人脸窗口；其次，将前端粗提取得到的人脸窗口作为后端卷积神经网络模型的输入进行特征精细提取，并通过池化操作得到新的特征图；最后，通过全连接层判别输出最佳检测窗口，完成人脸检测全过程。
结果
2
实验选取FDDB人脸检测数据集中包含人脸多姿态变化以及人脸面部特征信息不完整等情况的图像进行测试，TC_CNN方法人脸检测率达到96.39%，误检率低至3.78%，相比当前流行方法在保证算法效率的同时检测率均有提高。
结论
2
两层级联卷积神经网络人脸检测方法能够在人脸多姿态变化和面部特征信息不完整等情况下实现精准检测，保证较高的检测率，有效降低误检率，方法具有较好的鲁棒性和泛化能力。
Objective
2
As an important part of face recognition
face detection has attracted considerable attention in computer vision and has been widely investigated. Face detection determines the location and size of human faces in an image. Traditional face detection methods are limited by face multi-pose changes and incomplete facial features
which lead to their poor detection effect. Modern face detectors can easily detect near-frontal faces. Recent research in this area has focused on the uncontrolled face detection problem
where a number of factors
such as multi-pose changes and incomplete facial features
can lead to large visual variations in face appearance and can severely degrade the robustness of the face detector. A convolutional neural network can automatically select facial features
rapidly delete a large number of non-face background information
and can achieve good face detection results. However
a single convolutional neural network should possess three functions
namely
facial feature extraction
reduction of feature dimensions to decrease the computational complexity
and feature classification
which result in complex network structure
limited detection speed
and overfitting of the network. To solve these problems
this study presents a face detection method of two-layer cascaded convolutional neural network (TC_CNN).
Method
2
First
a two-layer convolutional neural network model is constructed. The first convolutional neural network model is used to extract the features of the face image
and a max pooling method is adopted to reduce the dimension of those features in which multiple suspected face windows are outputted. Second
the face windows are used as the inputs of the second convolutional neural network model for fine feature extraction
and a new feature map is obtained by pool operation. Finally
the best detection window is outputted through full connection layer discrimination. The face is successfully detected and the face window is returned when the result of discriminant classification is a face; otherwise
the non-face window is deleted. An optimal face detection window can be selected through non-maximum suppression
the size and position of the face in the input image are returned based on the location information of the optimal face detection window
and the entire process of face detection is completed. In the training of TC_CNN
we use 10 000 images with near-frontal faces
face multi-pose changes
and incomplete facial features from the labeled faces in the Wild dataset as positive training samples and 1 000 images as negative training samples. In the testing of the TC_CNN model
we utilize an authoritative dataset FDDB to evaluate
measure
and determine the validity of the model based on four indexes
namely
detection rate
false detection rate
missing detection rate
and detection time. The TC_CNN model is compared with excellent face detection algorithms
such as AdaBoost
fast LBP
NPD+AdaBoost
and SPP+CNN methods.
Result
2
Images with face multi-pose changes and incomplete face feature information in the FDDB face detection dataset are selected for the test. Results show that the face detection rate by TC_CNN method is up to 96.39%
false detection rate is as low as 3.78%
and detection time is 0.451 s. For the detection rate
the TC_CNN method is 7.63% higher than the traditional AdaBoost method based on cascade idea
3.57% higher than the fast LBP method
0.50% higher than the NPD+AdaBoost method
and 6.04% higher than the SPP+CNN method. For the false detection rate
the TC_CNN method is 2.44% lower than the AdaBoost method
4.47% lower than the fast LBP method
0.59% lower than the NPD+AdaBoost method
and 5.09% lower than the SPP+CNN method. For the detection time
the TC_CNN method's detection efficiency is remarkably higher than the SPP+CNN method and slightly higher than the AdaBoost
fast LBP
and NPD+AdaBoost methods. In comparison with the current methods
the detection rate is increased while ensuring the efficiency of the algorithm. To verify the robustness of the TC_CNN model under the conditions of face multi-pose changes and incomplete facial features
representative images of two special cases are selected from the FDDB dataset in conducting four groups of comparative experiments under the multi-pose changes of a single face image
multi-pose changes of a multi face image
incomplete facial features of a single face image
and incomplete facial features of a multi face image. Experimental results show that the TC_CNN model shows good effectiveness and robustness compared with the four excellent algorithms or four groups of contrastive experiments under different interference conditions.
Conclusion
2
The TC_CNN model for face detection can achieve accurate detection under face multi-pose changes and incomplete facial feature information. This model can obtain a high detection rate and effectively reduce false detection rate. The method has good robustness and generalization capability. The TC_CNN method overcomes the limitations of the excellent AdaBoost cascade concept on the face detection method (such as cascading two convolutional neural networks; effectively avoiding the complex network structure caused by the three functions of extraction
reduction
and classification of features simultaneously)
which easily cause overfitting and other contradictions. However
the selection of the number and parameter of the cascaded convolutional neural network is difficult for the improvement of the model performance and detection effect. In future research
we will determine the number and parameter of cascaded convolution neural network to optimize the model and will attempt to detect the size and position of the face accurately.
Hao Z K, Liu Y, Qin H W, et al. Scale-aware face detection[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI: IEEE, 2017: 1063-6919.[ DOI:10.1109/CVPR.2017.207 http://dx.doi.org/10.1109/CVPR.2017.207 ]
Campos N C S, Monteiro H A, Brito A V, et al. A framework for design and validation of face detection systems[C]//Proceedings of 2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies. Pucon, Chile: IEEE, 2017: 1-7.[ DOI:10.1109/CHILECON.2017.8229685 http://dx.doi.org/10.1109/CHILECON.2017.8229685 ]
Mukherjee S, Saha S, Lahiri S, et al. Convolutional Neural Network based face detection[C]//Proceedings of 20171st International Conference on Electronics, Materials Engineering and Nano-Technology. Kolkata, India: IEEE, 2017: 1-5.[ DOI:10.1109/IEMENTECH.2017.8076987 http://dx.doi.org/10.1109/IEMENTECH.2017.8076987 ]
Zhang S, Cai Y, Xie M. Face detection based on local region sparse coding[J]. Journal of Software, 2013, 24(11):2747-2757.
张抒, 蔡勇, 解梅.基于局部区域稀疏编码的人脸检测[J].软件学报, 2013, 24(11):2747-2757. [DOI:10.3724/SP.J.1001.2013.04484]
Song R. The research and implementation of human face detection and tracking based on complex background[D]. Hefei: Anhui University, 2014. http://cdmd.cnki.com.cn/Article/CDMD-10357-1014229556.htm .
宋茹.基于复杂场景的人脸检测与人脸跟踪的研究与实现[D].合肥: 安徽大学, 2014.
Bhandiwad V, Tekwani B. Face recognition and detection using neural networks[C] //Proceedings of 2017 International Conference on Trends in Electronics and Informatics. Tirunelveli, India: IEEE, 2017: 879-882.[ DOI:10.1109/ICOEI.2017.8300832 http://dx.doi.org/10.1109/ICOEI.2017.8300832 ]
Mohan K, Chandrasekhar P, Jilani S A K. A combined HOG-LPQ with Fuz-SVM classifier for Object face Liveness Detection[C]//Proceedings of 2017 International Conference on I-SMAC. Palladam, India: IEEE, 2017: 531-537.[ DOI:10.1109/I-SMAC.2017.8058406 http://dx.doi.org/10.1109/I-SMAC.2017.8058406 ]
Yu M, Yun L J, Chen Z Q, et al. Research on video face detection based on AdaBoost algorithm training classifier[C]//Proceedings of 2017 First International Conference on Electronics Instrumentation & Information Systems. Harbin, China: IEEE, 2017: 1-6.[ DOI:10.1109/EIIS.2017.8298627 http://dx.doi.org/10.1109/EIIS.2017.8298627 ]
Liu M Y. Face detection system based on static images[D]. Chengdu: University of Electronic Science and Technology of China, 2014. http://cdmd.cnki.com.cn/Article/CDMD-10614-1015706075.htm .
刘明跃.基于静态图像的人脸检测[D].成都: 电子科技大学, 2014.
Malisiewicz T, Gupta A, Efros A A. Ensemble of exemplar-SVMs for object detection and beyond[C]//Proceedings of 2011 IEEE International Conference on Computer Vision. Barcelona, Spain: IEEE, 2011: 89-96.[ DOI:10.1109/ICCV.2011.6126229 http://dx.doi.org/10.1109/ICCV.2011.6126229 ]
Wang Y Q. An analysis of the Viola-Jones face detection algorithm[J]. Image Processing On Line, 2014, 4:128-148.[DOI:10.5201/ipol.2014.104]
Tao Q Q. Face detection based on convolutional neural network and improved support vector machine[D]. Hefei: Hefei University of Technology, 2016. http://www.wanfangdata.com.cn/details/detail.do?_type=degree&id=Y3028330 .
陶勤勤.基于卷积神经网络和改进支持向量机的人脸检测[D].合肥: 合肥工业大学, 2016.
Li H X, Lin Z, Shen X H, et al. A convolutional neural network cascade for face detection[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA: IEEE, 2015: 5325-5334.[ DOI:10.1109/CVPR.2015.7299170 http://dx.doi.org/10.1109/CVPR.2015.7299170 ]
Qin H W, Yan J J, Li X, et al. Joint training of cascaded CNN for face detection[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE, 2016: 3456-3465.[ DOI:10.1109/CVPR.2016.376 http://dx.doi.org/10.1109/CVPR.2016.376 ]
Egorov A D, Divitskii D U, Dolgih A A, et al. Some cases of optimization face detection methodes on image (Using the Viola-Jones method as an example)[C]//Proceedings of 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering. Moscow, Russia: IEEE, 2018: 1075-1078.[ DOI:10.1109/EIConRus.2018.8317276 http://dx.doi.org/10.1109/EIConRus.2018.8317276 ]
Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada: Curran Associates Inc., 2012: 1097-1105.
Ye X Y, Chen X T, Chen H H, et al. Cascaded probability state-restricted Boltzmann machine for face detection[J]. Journal of Image and Graphics, 2016, 21(7):875-885.
叶学义, 陈雪婷, 陈华华, 等.级联型P-RBM神经网络的人脸检测[J].中国图象图形学报, 2016, 21(7):875-885. [DOI:10.11834/jig.20160705]
Tang Y T, Liang R, Liu C. Face detection system design based on the SOPC[J]. Electronic Design Engineering, 2014, (21):165-168.
汤欲涛, 梁锐, 刘畅.基于SOPC的人脸检测系统的设计[J].电子设计工程, 2014, (21):165-168.
Bilaniuk O, Fazl-Ersi E, Laganière R, et al. Fast LBP face detection on low-power SIMD architectures[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Columbus, OH: IEEE, 2014: 616-622.[ DOI:10.1109/CVPRW.2014.96 http://dx.doi.org/10.1109/CVPRW.2014.96 ]
Micheal A A, Geetha P. Multi-view face detection using Normalized Pixel Difference feature[C]//Proceedings of 2017 International Conference on Communication and Signal Processing. Chennai, India: IEEE, 2017: 988-992.[ DOI:10.1109/ICCSP.2017.8286520 http://dx.doi.org/10.1109/ICCSP.2017.8286520 ]
Zheng C H, Liu B, Zhou Y. Face detection algorithm based on scale-independent cascade convolution neural network[J]. Application Research of Computers, 2019, 36(3).
郑成浩, 刘兵, 周勇.尺度无关的级联卷积神经网络人脸检测算法[J].计算机应用研究, 2019, 36(3).
Lu H T, Zhang Q C. Applications of deep convolutional neural network in computer vision[J]//Journal of Data Acquisition and Processing, 2016, 31(1):1-17.
卢宏涛, 张秦川.深度卷积神经网络在计算机视觉中的应用研究综述[J].数据采集与处理, 2016, 31(1):1-17. [DOI:10.16337/j.1004-9037.2016.01.001]
相关作者
相关机构
京公网安备11010802024621
