Abstract
Accuracy and efficiency are two conflicting challenges for face detection, since effective models tend to be computationally prohibitive. To address these two conflicting challenges, our core idea is to shrink the input image and focus on detecting small faces. Specifically, we propose a novel face detector, dubbed the name Densely Connected Face Proposal Network (DCFPN), with high performance as well as real-time speed on the CPU devices. On the one hand, we subtly design a lightweight-but-powerful fully convolutional network with the consideration of efficiency and accuracy. On the other hand, we use the dense anchor strategy and propose a fair L1 loss function to handle small faces well. As a consequence, our method can detect faces at 30 FPS on a single 2.60 GHz CPU core and 250 FPS using a GPU for the VGA-resolution images. We achieve state-of-the-art performance on the AFW, PASCAL face and FDDB datasets.
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References
Viola, P., Jones, M.J.: Robust real-time face detection. IJCV 57(2), 137–154 (2004)
Zhang, C., Zhang, Z.: A survey of recent advances in face detection. Technical report (2010)
Lecun, Y., Bengio, Y.: Convolutional networks for images, speech, and time-series. In: The Handbook of Brain Theory and Neural Networks (1995)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR (2014)
Li, H., Lin, Z., Shen, X., Brandt, J., Hua, G.: A convolutional neural network cascade for face detection. In: CVPR (2015)
Zhang, S., Zhu, X., Lei, Z., Shi, H., Wang, X., Li, S.Z.: FaceBoxes: a CPU real-time face detector with high accuracy. In: IJCB (2017)
Yang, M.H., Kriegman, D.J., Ahuja, N.: Detecting faces in images: a survey. PAMI 24(1), 34–58 (2002)
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)
Yang, B., Yan, J., Lei, Z., Li, S.Z.: Aggregate channel features for multi-view face detection. In: IJCB (2014)
Zhang, L., Chu, R., Xiang, S., Liao, S., Li, S.Z.: Face detection based on multi-block LBP representation. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 11–18. Springer, Heidelberg (2007). doi:10.1007/978-3-540-74549-5_2
Huang, C., Ai, H., Li, Y., Lao, S.: High-performance rotation invariant multiview face detection. PAMI 29(4), 671–686 (2007)
Jones, M., Viola, P.: Fast multi-view face detection. In: MERL (2003)
Zhang, C., Platt, J.C., Viola, P.A.: Multiple instance boosting for object detection. In: NIPS (2005)
Bourdev, L., Brandt, J.: Robust object detection via soft cascade. In: CVPR (2005)
Li, S.Z., Zhu, L., Zhang, Z.Q., Blake, A., Zhang, H.J., Shum, H.: Statistical learning of multi-view face detection. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 67–81. Springer, Heidelberg (2002). doi:10.1007/3-540-47979-1_5
Xiao, R., Zhu, L., Zhang, H.J.: Boosting chain learning for object detection. In: ICCV (2003)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. PAMI 32(9), 1627–1645 (2010)
Ghiasi, G., Fowlkes, C.C.: Occlusion coherence: detecting and localizing occluded faces. arXiv preprint arXiv:1506.08347 (2015)
Mathias, M., Benenson, R., Pedersoli, M., Gool, L.: Face detection without bells and whistles. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 720–735. Springer, Cham (2014). doi:10.1007/978-3-319-10593-2_47
Yan, J., Lei, Z., Wen, L., Li, S.Z.: The fastest deformable part model for object detection. In: CVPR (2014)
Yan, J., Zhang, X., Lei, Z., Li, S.Z.: Face detection by structural models. Image Vis. Comput. 32(10), 790–799 (2014)
Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: CVPR (2012)
Yang, B., Yan, J., Lei, Z., Li, S.Z.: Convolutional channel features. In: ICCV (2015)
Yang, S., Luo, P., Loy, C.C., Tang, X.: From facial parts responses to face detection: a deep learning approach. In: ICCV (2015)
Chen, D., Hua, G., Wen, F., Sun, J.: Supervised transformer network for efficient face detection. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 122–138. Springer, Cham (2016). doi:10.1007/978-3-319-46454-1_8
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)
Yang, S., Luo, P., Loy, C., Tang, X.: Wider face: a face detection benchmark. In: CVPR (2016)
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: ACM MM (2014)
Shen, X., Lin, Z., Brandt, J., Wu, Y.: Detecting and aligning faces by image retrieval. In: CVPR (2013)
Jain, V., Learned-Miller, E.G.: Fddb: a benchmark for face detection in unconstrained settings. UMass Amherst Report (2010)
Acknowledgments
This work was supported by the National Key Research and Development Plan (Grant No. 2016YFC0801002).
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Zhang, S., Zhu, X., Lei, Z., Shi, H., Wang, X., Li, S.Z. (2017). Detecting Face with Densely Connected Face Proposal Network. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_1
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DOI: https://doi.org/10.1007/978-3-319-69923-3_1
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