Patel, R. Chellappa, A deep pyramid deformable part model for face detection, in 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS) (IEEE, 2015), pp. Loy, X. Tang, From facial parts responses to face detection: a deep learning approach, in Proceedings of the IEEE International Conference on Computer Vision (2015), pp. Li, Multi-view face detection using deep convolutional neural networks, in Proceedings of the 5th ACM on International Conference on Multimedia Retrieval (ACM, 2015), pp. H. Li, Z. Lin, X. Shen, J. Brandt, G. Hua, A convolutional neural network cascade for face detection, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. S. Liao, A. Jain, S. Li, A fast and accurate unconstrained face detector (2014) Fowlkes, Occlusion coherence: detecting and localizing occluded faces (2015). Li, Aggregate channel features for multi-view face detection, in 2014 IEEE International Joint Conference on Biometrics (IJCB) (IEEE, 2014), pp. Sun, Joint cascade face detection and alignment, in Computer Vision-ECCV 2014 (Springer, Berlin, 2014), pp. Van Gool, Face detection without bells and whistles, in Computer Vision-ECCV 2014 (Springer, Berlin, 2014), pp. H. Li, Z. Lin, J. Brandt, X. Shen, G. Hua, Efficient boosted exemplar-based face detection, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014), pp. Pandžić, J. Ahlberg, R. Forchheimer, A method for object detection based on pixel intensity comparisons organized in decision trees (2013). H. Li, G. Hua, Z. Lin, J. Brandt, J. Yang, Probabilistic elastic part model for unsupervised face detector adaptation, in Proceedings of the IEEE International Conference on Computer Vision (2013), pp. J. Li, Y. Zhang, Learning surf cascade for fast and accurate object detection, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2013), pp. X. Zhu, D. Ramanan, Face detection, pose estimation, and landmark localization in the wild, in 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2012), pp. I–511Ĭ. Zhang, Z. Zhang, A survey of recent advances in face detection. P. Viola, M. Jones, Rapid object detection using a boosted cascade of simple features, in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001. Loy, X. Tang, Wider face: a face detection benchmark, in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2016), pp. The experimental results show that our proposed approach trained on WIDER FACE Dataset outperforms strong baselines on WIDER FACE Dataset by a large margin, and consistently achieves competitive results on FDDB against the recent state-of-the-art face detection methods. The proposed approach is benchmarked on two recent challenging face detection databases, i.e., the WIDER FACE Dataset which contains high degree of variability, as well as the Face Detection Dataset and Benchmark (FDDB). Second, our proposed network allows explicit body contextual reasoning in the network inspired from the intuition of human vision system.
![cms region x cms region x](https://www.pdffiller.com/preview/6/962/6962977/large.png)
First, the multi-scale information is grouped both in region proposal and RoI detection to deal with tiny face regions.
![cms region x cms region x](http://www.cmslogistics.ae/wp-content/uploads/2016/10/cms_slideinforwm10.jpg)
However, far apart of that network, there are two main contributions in our proposed network that play a significant role to achieve the state-of-the-art performance in face detection. Similar to the region-based CNNs, our proposed network consists of the region proposal component and the region-of-interest (RoI) detection component.
![cms region x cms region x](https://cache.pressmailing.net/thumbnail/story_hires/da0a47f9-d381-4234-a8ac-8b6ee4577dfc/image.jpg)
In this paper, we present a face detection approach named Contextual Multi-Scale Region-based Convolution Neural Network (CMS-RCNN) to robustly solve the problems mentioned above. Although the face detection problem has been intensely studied for decades with various commercial applications, it still meets problems in some real-world scenarios due to numerous challenges, e.g., heavy facial occlusions, extremely low resolutions, strong illumination, exceptional pose variations, image or video compression artifacts, etc. Robust face detection in the wild is one of the ultimate components to support various facial related problems, i.e., unconstrained face recognition, facial periocular recognition, facial landmarking and pose estimation, facial expression recognition, 3D facial model construction, etc.