Abstract
For highly occluded faces, only few features exist, which makes such face detection more challenging. In this paper, we propose a novel algorithm to make full use of the facial features. The proposed algorithm is based on Region-based Fully Convolutional Network (R-FCN) with two improved parts for robust face detection, including the multi-scale training and a new feature-fusion scheme. Firstly, instead of utilizing fixed scales for all faces, we adopt multi-scale inputs to strengthen the features of the partial faces and increase the training set diversity. Up-sampling the training images can efficiently enlarge the features of the occluded faces. Secondly, we make a feature fusion by combining layers with different sizes of receptive fields, which can preserve the details of the faces with only partial faces available. Our method achieves superior accuracy over the stat-of-the-art techniques on massively-benchmarked face dataset (WIDER FACE), and shows great improvements for highly occluded face detection.
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References
Zafeiriou, S., Zhang, C., Zhang, Z.: A survey on face detection in the wild: past, present and future. Comput. Vis. Image. Underst. 138, 1–24 (2015)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 27th IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587. IEEE Press, Columbus (2014)
Dai, J., Li, Y., He, K., Sun, J.: R-FCN: object detection via region-based fully convolutional networks. In: 30th Conference on Neural Information Processing Systems, Barcelona, pp. 379–387 (2016)
Zeiler, M.D., Krishnan, D., Taylor, G.W., Fergus, R.: Deconvolutional networks. In: 23th IEEE Conference on Computer Vision and Pattern Recognition, pp. 2528–2535. IEEE Press, San Francisco (2010)
Yang, S., Luo, P., Loy, C.C., Tang, X.: Wider face: a face detection benchmark. In: 27th IEEE Conference on Computer Vision and Pattern Recognition, pp. 5525–5533. IEEE Press, Las Vegas (2016)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: 14th IEEE Conference on Computer Vision and Pattern Recognition, pp. 511–518. IEEE Press, Hawaii (2001)
Mita, T., Kaneko, T., Hori, O.: Joint Haar-like features for face detection. In: 10th IEEE International Conference on Computer Vision, pp. 1619–1626. IEEE Press, Beijing (2005)
Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004). doi:10.1007/978-3-540-24670-1_36
Zhang, G., Huang, X., Li, S.Z., Wang, Y., Wu, X.: Boosting local binary pattern (LBP)-Based face recognition. In: 5th Chinese Conference on Advances in Biometric Person Authentication, Guangzhou, pp. 179–186 (2004)
Liu, C., Shum, H.Y.: Kullback-leibler boosting. In: 16th IEEE Conference on Computer Vision and Pattern Recognition, pp. 587–594. IEEE Press, Madison (2003)
Meynet, J., Popovici, V., Thiran, J.P.: Face detection with boosted Gaussian features. Pattern Recognit. 40, 2283–2291 (2007)
Chen, X., Gu, L., Li, S.Z., Zhang, H.J.: Learning representative local features for face detection. In: 14th IEEE Conference on Computer Vision and Pattern Recognition, pp. 1126–1131. IEEE Press, Hawaii (2001)
Levi, K., Weiss, Y.: Learning object detection from a small number of examples: the importance of good features. In: 17th IEEE Conference on Computer Vision and Pattern Recognition, pp. 53–60. IEEE Press, Washington (2004)
Dalal, N., Triggs, B.: Histogram of oriented gradients for human detection. In: 18th IEEE Conference on Computer Vision and Pattern Recognition, pp. 886–893. IEEE Press, San Diego (2005)
Liu, C.: A Bayesian discriminating features method for face detection. IEEE Trans. Pattern Anal. Mach. Intell. 25, 725–740 (2003)
Li, Y., Gong, S., Liddell, H.: Support vector regression and classification based multi-view face detection and recognition. In: 4th IEEE International Conference on Automatic Face and Gesture Recognition, p. 300. IEEE Press, Grenoble (2000)
Wang, P., Ji, Q.: Multi-view face detection under complex scene based on combined SVMs. In: 17th International Conference on Pattern Recognition, Cambridge, pp. 179–182 (2004)
Li, H., Lin, Z., Shen, X., Brandt, J., Hua, G.: A convolutional neural network cascade for face detection. In: 28th IEEE Conference on Computer Vision and Pattern Recognition, pp. 5325–5334. IEEE Press, Boston (2015)
Li, Y., Sun, B., Wu, T., Wang, Y.: Face detection with endto-end integration of a convnet and a 3d model. arXiv preprint arxiv: 1606.00850 (2016)
Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multi-task cascaded convolutional networks. arXiv preprint arxiv: 1604.02878 (2016)
Sun, X., Wu, P., Hoi, S.C.H.: Face detection using deep learning: an improved faster rcnn approach. arXiv preprint arxiv: 1701.08289 (2017)
Hu, P., Ramanan, D.: Finding Tiny Faces. arXiv preprint arxiv: 1612.04402 (2017)
Girshick, R.: Fast R-CNN. In: 20th IEEE International Conference on Computer Vision, pp. 1440–1448. IEEE Press, Santiago (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 27th IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778. IEEE Press, Las Vegas (2016)
Russakovsky, O., Deng, J., Su, H.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211–252 (2015)
Shrivastava, A., Gupta, A., Girshick, R.: Training region-based object detectors with online hard example mining. In: 27th IEEE Conference on Computer Vision and Pattern Recognition, pp. 761–769. IEEE Press, Las Vegas (2016)
Jia, Y.Q., Shelhamer, E., Donahue, J.: Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arxiv: 1408.5093 (2014)
Acknowledgements
The authors would like to thank the editor and all the anonymous reviewers of this paper for their constructive suggestions and comments. This work is supported by NSFC (No. 61671290) in China, the Key Program for International S&T Cooperation Project of China (No. 2016YFE0129500), and the Shanghai Committee of Science and Technology, China (No. 17511101903).
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Liu, L., Jiang, F., Shen, R. (2017). Highly Occluded Face Detection: An Improved R-FCN Approach. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10639. Springer, Cham. https://doi.org/10.1007/978-3-319-70136-3_63
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