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2016 | OriginalPaper | Buchkapitel

Grid Loss: Detecting Occluded Faces

verfasst von : Michael Opitz, Georg Waltner, Georg Poier, Horst Possegger, Horst Bischof

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

Detection of partially occluded objects is a challenging computer vision problem. Standard Convolutional Neural Network (CNN) detectors fail if parts of the detection window are occluded, since not every sub-part of the window is discriminative on its own. To address this issue, we propose a novel loss layer for CNNs, named grid loss, which minimizes the error rate on sub-blocks of a convolution layer independently rather than over the whole feature map. This results in parts being more discriminative on their own, enabling the detector to recover if the detection window is partially occluded. By mapping our loss layer back to a regular fully connected layer, no additional computational cost is incurred at runtime compared to standard CNNs. We demonstrate our method for face detection on several public face detection benchmarks and show that our method outperforms regular CNNs, is suitable for realtime applications and achieves state-of-the-art performance.

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Benenson, R., Mathias, M., Tuytelaars, T., Van Gool, L.: Seeking the Strongest Rigid Detector. In: Proceedings of the CVPR (2013)

Dollár, P., Appel, R., Belongie, S., Perona, P.: Fast feature pyramids for object detection. PAMI 36(8), 1532–1545 (2014)CrossRef

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, Heidelberg (2014). doi:10.1007/978-3-319-10593-2_47

Schulter, S., Leistner, C., Wohlhart, P., Roth, P.M., Bischof, H.: Accurate object detection with joint classification-regression random forests. In: Proceedings of the CVPR (2014)

Viola, P., Jones, M.J.: Robust real-time face detection. IJCV 57(2), 137–154 (2004)CrossRef

Zhang, S., Benenson, R., Schiele, B.: Filtered channel features for pedestrian detection. In: Proceedings of the CVPR (2015)

Yang, B., Yan, J., Lei, Z., Li, S.Z.: Convolutional channel features. In: Proceedings of the ICCV (2015)

Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. PAMI 32(9), 1627–1645 (2010)CrossRef

Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS (2012)

10.

Farfade, S.S., Saberian, M., Li, L.J.: Multi-view face detection using deep convolutional neural networks. In: Proceedings of the ICMR (2015)

11.

Hosang, J., Omran, M., Benenson, R., Schiele, B.: Taking a deeper look at pedestrians. In: Proceedings of the CVPR (2015)

12.

Li, H., Lin, Z., Shen, X., Brandt, J., Hua, G.: A convolutional neural network cascade for face detection. In: Proceedings of the CVPR (2015)

13.

Sermanet, P., Kavukcuoglu, K., Chintala, S., LeCun, Y.: Pedestrian detection with unsupervised multi-stage feature learning. In: Proceedings of the CVPR (2013)

14.

Jain, V., Learned-Miller, E.: FDDB: A benchmark for face detection in unconstrained settings. Technical report UM-CS-2010-009, University of Massachusetts, Amherst (2010)

15.

Yan, J., Zhang, X., Lei, Z., Li, S.Z.: Face detection by structural models. IVC 32(10), 790–799 (2014)CrossRef

16.

Zhu, X., Ramanan, D.: Face detection, pose estimation and landmark estimation in the wild. In: Proceedings of the CVPR (2012)

17.

Zafeiriou, S., Zhang, C., Zhang, Z.: A survey on face detection in the wild: past present and future. CVIU 138, 1–24 (2015)

18.

Li, J., Zhang, Y.: Learning surf cascade for fast and accurate object detection. In: Proceedings of the CVPR (2013)

19.

Li, H., Lin, Z., Brandt, J., Shen, X., Hua, G.: Efficient boosted exemplar-based face detection. In: Proceedings of the CVPR (2014)

20.

Chen, D., Ren, S., Wei, Y., Cao, X., Sun, J.: Joint cascade face detection and alignment. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 109–122. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10599-4_8

21.

Yang, B., Yan, J., Lei, Z., Li, S.Z.: Aggregate channel features for multi-view face detection. In: Proceedings of the IJCB (2014)

22.

Ghiasi, G., Fowlkes, C.C.: Occlusion coherence: localizing occluded faces with a hierarchical deformable part model. In: Proceedings of the CVPR (2014)

23.

Yan, J., Lei, Z., Wen, L., Li, S.: The fastest deformable part model for object detection. In: Proceedings of the CVPR (2014)

24.

Li, H., Hua, G., Lin, Z., Brandt, J., Yang, J.: Probabilistic elastic part model for unsupervised face detector adaptation. In: Proceedings of the ICCV (2013)

25.

Shen, X., Lin, Z., Brandt, J., Wu, Y.: Detecting and aligning faces by image retrieval. In: Proceedings of the CVPR (2013)

26.

Kumar, V., Namboodiri, A.M., Jawahar, C.V.: Visual phrases for exemplar face detection. In: Proceedings of the ICCV (2015)

27.

Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the CVPR (2014)

28.

Garcia, C., Delakis, M.: Convolutional face finder: a neural architecture for fast and robust face detection. PAMI 26(11), 1408–1423 (2004)CrossRef

29.

Rowley, H., Baluja, S., Kanade, T., et al.: Neural network-based face detection. PAMI 20(1), 23–38 (1998)CrossRef

30.

Vaillant, R., Monrocq, C., LeCun, Y.: Original approach for the localisation of objects in images. IEEE Proc. Vis., Image Sig. Proces. 141(4), 245–250 (1994)CrossRef

31.

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge, IJCV, pp. 1–42 (2015)

32.

Yang, S., Luo, P., Loy, C.C., Tang, X.: From facial parts responses to face detection: a deep learning approach. In: Proceedings of the ICCV (2015)

33.

Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: OverFeat: integrated recognition, localization and detection using convolutional networks. In: Proceedings of the ICLR (2014)

34.

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. JMLR 15(1), 1929–1958 (2014)MathSciNetMATH

35.

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the CVPR (2015)

36.

Köstinger, M., Wohlhart, P., Roth, P.M., Bischof, H.: Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization. In: Proceedings of the BeFIT (in conj. with ICCV) (2011)

37.

Everingham, M., Eslami, S.M.A., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The Pascal visual object classes challenge: a retrospective. IJCV 111(1), 98–136 (2015)CrossRef

38.

Lee, C.Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: Deeply-supervised nets. In: Proceedings of the AISTATS (2015)

39.

Burgos-Artizzu, X., Perona, P., Dollár, P.: Robust face landmark estimation under occlusion. In: Proceedings of the ICCV (2013)

40.

Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of the ICLR (2015)

41.

Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I.J., Bergeron, A., Bouchard, N., Bengio, Y.: Theano: new features and speed improvements. In: Proceedings of the NIPS Deep Learning Workshop (2012)

42.

Dubout, C., Fleuret, F.: Exact acceleration of linear object detectors. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7574, pp. 301–311. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33712-3_22

43.

Girshick, R., Iandola, F., Darrell, T., Malik, J.: Deformable part models are convolutional neural networks. In: Proceedings of the CVPR (2015)

44.

Jaderberg, M., Vedaldi, A., Zisserman, A.: Speeding up convolutional neural networks with low rank expansions. In: Proceedings of the BMVC (2014)

45.

Zhang, X., Zou, J., He, K., Sun, J.: Accelerating very deep convolutional networks for classification and detection (2015). arXiv:abs/1505.06798

Titel: Grid Loss: Detecting Occluded Faces
verfasst von: Michael Opitz
Georg Waltner
Georg Poier
Horst Possegger
Horst Bischof
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2016
Print ISBN: 978-3-319-46486-2

Electronic ISBN: 978-3-319-46487-9

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-46487-9_24

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