In the unconstrained open-space, face detection is still a challenging task due to the facial posture changes, complex background environment, and motion blur. The rotation-invariant algorithm based on cascaded network and pyramid optical flow is proposed. Firstly, the cascading progressive convolutional neural network is adopted to locate the face position and facial landmark of the previous frame in the video stream. Secondly, the in-dependent facial landmark detection network is used to reposition the current frame, and the optical flow mapping displacement of the facial landmark between the two frames is calculated afterwards. Finally, the detected face is corrected by the mapping relationship between the optical flow displacement of the facial landmark and the bounding box, thereby completing the rotation-invariant face detection. The experiment was tested on the FDDB public datasets, which proved that the method is more accurate. Moreover, the dynamic test on the Boston Head Tracking Database proves that the face detection algorithm can effectively solve the problem of rotation-invariant face detection. Compared with other detection algorithms, the detection speed of the proposed algorithm has a great advantage, and the window jitter problem in the video is well solved.
Rotating invariant face detection via cascaded networks and pyramidal optical flows
First published at:Jan 14, 2020
 Viola P, Jones M. Rapid object detection using a boosted cascade of simple features[C]//Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001.
 Li B, Yang A M, Yang J. Rotated face detection using AdaBoost[C]//Proceedings of 2010 2nd International Conference on Information Engineering and Computer Science, 2010: 1–4.
 Froba B, Ernst A. Face detection with the modified census transform[C]//Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004: 91–96.
 Jin H L, Liu Q S, Lu H Q, et al. Face detection using improved LBP under Bayesian framework[C]//Proceedings of the Third International Conference on Image and Graphics, 2004: 306–309.
 Farfade S S, Saberian M J, Li L J. Multi-view face detection using deep convolutional neural networks[C]//Proceedings of the 5th ACM on International Conference on Multimedia Re-trieval, 2015: 643–650.
 Ranjan R, Patel V M, Chellappa R. A deep pyramid deformable part model for face detection[C]//Proceedings of 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems, 2015.
 Yang S, Luo P, Loy C C, et al. From facial parts responses to face detection: a deep learning approach[C]//Proceedings of 2015 IEEE International Conference on Computer Vision, 2015.
 Bas A, Huber P, Smith W A P, et al. 3D morphable models as spatial transformer networks[C]//Proceedings of 2017 IEEE International Conference on Computer Vision Workshops, 2017.
 Li X X, Liang R H. A review for face recognition with occlusion: from subspace regression to deep learning[J]. Chinese Journal of Computers, 2018, 41(1): 177–207.
李小薪, 梁荣华. 有遮挡人脸识别综述: 从子空间回归到深度学习[J]. 计算机学报, 2018, 41(1): 177–207.
 Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal net-works[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems, 2015: 91–99.
 Liu W, Anguelov D, Erhan D, et al. Single shot MultiBox detector[C]//Proceedings of the 14th European Conference on Compute Vision (ECCV), 2016: 21–37.
 Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016.
 Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[C]//Proceedings of the 3rd International Conference on Learning Representations, 2015.
 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, 2015: 5325–5334.
 Pan R, Wei H Q. Research on human face detection and recognition based on rotation invariance[J]. Computer Engi-neering and Design, 2009, 30(8): 1941–1943, 1997.
潘榕, 魏慧琴. 基于旋转不变性的人脸定位识别研究[J]. 计算机工程与设计, 2009, 30(8): 1941–1943, 1997.
 Wang W Q, Zhang X Y, Gao C Q, et al. Scale invariant face recognition from single sample[J]. Journal of Image and Graphics, 2012, 17(3): 380–386.
王炜强, 张晓阳, 曹春芹, 等. 尺度不变单样本人脸识别方法[J]. 中国图象图形学报, 2012, 17(3): 380–386.
 Bao X A, Hu L L, Zhang N, et al. Fast face detection algorithm based on cascade network[J]. Journal of Zhejiang Sci-Tech University, 2019, 41(3): 347–353.
包晓安, 胡玲玲, 张娜, 等. 基于级联网络的快速人脸检测算法[J]. 浙江理工大学学报, 2019, 41(3): 347–353.
 Liu W Q. Research on face detection algorithm based on cascaded convolutional neural networks[D]. Xiamen: Xiamen University, 2017.
刘伟强. 基于级联卷积神经网络的人脸检测算法的研究[D]. 厦门: 厦门大学, 2017.
 Sun K, Li Q M, Li D Q. Face detection algorithm based on cascaded convolutional neural network[J]. Journal of Nanjing University of Science and Technology, 2018, 42(1): 40–47.
孙康, 李千目, 李德强. 基于级联卷积神经网络的人脸检测算法[J]. 南京理工大学学报, 2018, 42(1): 40–47.
 Lin L Y. A visual object tracking method via CNN and optical flow with online learning[D]. Guangzhou: Guangdong University of Technology, 2018.
林露樾. 融合卷积神经网络以及光流法的目标跟踪方法[D]. 广州: 广东工业大学, 2018.
 Wang Z L, Huang M, Zhu Q B, et al. The optical flow detection method of moving target using deep convolution neural net-work[J]. Opto-Electronic Engineering, 2018, 45(8): 38–47.
王正来, 黄敏, 朱启兵, 等. 基于深度卷积神经网络的运动目标光流检测方法[J]. 光电工程, 2018, 45(8): 38–47.
 Zhang K P, Zhang Z P, Li Z F, et al. Joint face detection and alignment using multitask cascaded convolutional networks[J]. IEEE Signal Processing Letters, 2016, 23(10): 1499–1503.
 Yang S, Luo P, Loy C C, et al. WIDER FACE: a face detection benchmark[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016.
 Liu Z W, Luo P, Wang X G, et al. Deep learning face attributes in the wild[C]//Proceedings of 2015 IEEE International Confe-rence on Computer Vision, 2015: 3730–3738.
 Sun Y, Wang X G, Tang X O. Deep convolutional network cascade for facial point detection[C]//Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013: 3476–3483.
 K?stinger M, Wohlhart P, Roth P M, et al. Annotated facial landmarks in the wild: a large-scale, real-world database for facial landmark localization[C]//Proceedings of 2011 IEEE International Conference on Computer Vision Workshops, 2011: 2144–2151.
 Jain V, Learned-Miller E G. FDDB: A benchmark for face detection in unconstrained settings[R]. UMass Amherst Technical Report, 2010.
 Cascia M L, Sclaroff S. Fast, reliable head tracking under varying illumination[C]// Proceedings of 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999: 604–610.
 Wang H, Li Z F, Ji X, et al. Face R-CNN[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017.
National Natural Science Foundation of China (61471154) and Fundamental Research Funds for Central Universities (JZ2018YYPY0287)
Get Citation: Sun Rui, Kan Junsong, Wu Liuwei, et al. Rotating invariant face detection via cascaded networks and pyramidal optical flows[J]. Opto-Electronic Engineering, 2020, 47(1): 190135.