4. 文中提出了尺度敏感的Data-anchor-取樣策略，改變訓練樣本的分佈，重點關注了較小的人臉。

結束語

人臉做為計算機視覺的一個大的研究方向，很多科研人員在上面投入了大量精力，每年出來上百篇相關論文，本文中不一一列舉，文中講述分析如有不妥之處請多包涵指正！

參考文獻

[1] Henry A Rowley, Shumeet Baluja, Takeo Kanade. Neural network-based face detection. 1998, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2] Henry A Rowley, Shumeet Baluja, Takeo Kanade. Rotation invariant neural network-based face detection. 1998, computer vision and pattern recognition.

[3] P.Viola and M.Jones. Rapid object detection using a boosted cascade of simple features. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition, 2001

[4] Rainer Lienhart, Jochen Maydt. An extended set of Haar-like features for rapid object detection. 2002, international conference on image processing.

[5] Lubomir Bourdev, Jonathan Brandt. Robust Object Detection Via Soft Cascade. CVPR 2005.

[6] Bo Wu, Haizhou Ai, Chang Huang, Shihong Lao. Fast rotation invariant multi-view face detection based on real Adaboost. 2004, IEEE international conference on automatic face and gesture recognition.

[7] M.Jones, P.Viola. Fast Multi-View Face Detection. Mitsubishi Electric Research Laboratories, Technical Report: MERL-2003-96, July 2003.

[8] Y.Ma, X.Q.Ding. Real-time rotation invariant face detection based on cost-sensitive AdaBoost. In: Proceedings of the IEEE International Conference on Image Processing. Barcelona, Spain: IEEE Computer Society, 2003. 921-924.

[9] Y.Ma, X.Q.Ding. Robust multi-view face detection and pose estimation based on cost-sensitive AdaBoost. In: Proceedings of the 6-th Asian Conference on Computer Vision. Jeju, Korea: Springer, 2004.

[10] S.Z.Li, L.Zhu, Z.Q.Zhang, A.Blake, H.J.Zhang, H.Y.Shum. Statistical learning of multi-view face detection. In: Proceedings of the 7-th European Conference on Computer Vision. Copenhagen, Denmark: Springer, 2002.67-81.

[11] S.Z.Li, Z.Q.Zhang, H.Y.Shum, H.J.Zhang. FloatBoost learning for classification. In: Proceedings of the 16-th Annual Conference on Neural Information Processing Systems. Vancouver, Canada: MIT Press, 2002. 993-1000.

[12] S.Z.Li, L.Zhu, Z.Q.Zhang, H.J.Zhang. Learning to detect multi-view faces in real-time. In: Proceedings of the 2-nd International Conference on Development and Learning. Cambridge, MA, USA: IEEE Computer Society, 2002. 172-177.

[13] S.Z.Li, Z.Q.Zhang. FloatBoost Learning and Statistical Face Detection. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004.

[14] B.Wu, H.Z.Ai, C.Huang, S.H.Lao. Fast rotation invariant multi-view face detection based on real adaboost. In: Proc. 6th Int'l Conf. Automatic Face and Gesture Recognition, 2004, 79-84.

[15] B. Yang, J. Yan, Z. Lei and S. Z. Li. Aggregate channel features for multi-view face detection.. International Joint Conference on Biometrics, 2014.

[16] M. Mathias, R. Benenson, M. Pedersoli and L. Van Gool. Face detection without bells and whistles. ECCV 2014.

[17] Haoxiang Li, Zhe Lin, Xiaohui Shen, Jonathan Brandt, Gang Hua. A convolutional neural network cascade for face detection. 2015, computer vision and pattern recognition

[18] Lichao Huang, Yi Yang, Yafeng Deng, Yinan Yu. DenseBox: Unifying Landmark Localization with End to End Object Detection. 2015, arXiv: Computer Vision and Pattern Recognition

[19] Shuo Yang, Ping Luo, Chen Change Loy, Xiaoou Tang. Faceness-Net: Face Detection through Deep Facial Part Responses.

[20] Kaipeng Zhan, Zhanpeng Zhang, Zhifeng L, Yu Qiao. Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks. 2016, IEEE Signal Processing Letters.

[21] HR - P. Hu, D. Ramanan. Finding Tiny Faces. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

[22] Face R-CNN - H. Wang, Z. Li, X. Ji, Y. Wang. Face R-CNN. arXiv preprint arXiv:1706.01061, 2017.

[23] SSH - M. Najibi, P. Samangouei, R. Chellappa, L. Davis. SSH: Single Stage Headless Face Detector. IEEE International Conference on Computer Vision (ICCV), 2017.

[24] PyramidBox - X. Tang, Daniel K. Du, Z. He, J. Liu PyramidBox: A Context-assisted Single Shot Face Detector. arXiv preprint arXiv:1803.07737, 2018.

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