Multi-scale Fully Convolutional Network for Face Detection in the Wild

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Volumn 2017-July, Issue , 2017, Pages 2078-2087

  Bai, Yancheng ^a,b   Ghanem, Bernard ^a  

^a KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Saudi Arabia)

^b INSTITUTE OF SOFTWARE   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; CONVOLUTION; PATTERN RECOGNITION;

CLASSICAL PROBLEMS; CONVOLUTIONAL NETWORKS; END TO END; FEATURE MAP; FEATURE PYRAMID; SCALE MODELING; STATE-OF-THE-ART METHODS; UP SAMPLING;

FACE RECOGNITION;

EID: 85030250936     PISSN: 21607508     EISSN: 21607516     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2017.259     Document Type: Conference Paper

References (50)

1. S. Bell, C. L. Zitnick, K. Bala, and R. Girshick. Insideoutside net: Detecting objects in context with skip pooling and recurrent neural networks. Computer Vision and Pattern Recognition (CVPR), 2016.
2. Z. Cai, Q. Fan, R. S. Feris, and N. Vasconcelos. A unified multi-scale deep convolutional neural network for fast object detection. In Computer Vision-ECCV 2016-14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part IV, pages 354-370, 2016.
3. D. Chen, G. Hua, F. Wen, and J. Sun. Supervised Transformer Network for Efficient Face Detection, pages 122-138. Springer International Publishing, Cham, 2016.
4. D. Chen, S. Ren, Y. Wei, X. Cao, and J. Sun. Joint cascade face detection and alignment. In Computer Vision-ECCV 2014, pages 109-122. Springer, 2014.
5. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 886-893. IEEE, 2005.
6. S. S. Farfade, M. J. Saberian, and L.-J. Li. Multi-view face detection using deep convolutional neural networks. In Pro-ceedings of the 5th ACM on International Conference on Multimedia Retrieval, pages 643-650. ACM, 2015.
7. P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained partbased models. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 32 (9):1627-1645, 2010.
8. R. Girshick. Fast r-cnn. In Proceedings of the IEEE International Conference on Computer Vision, pages 1440-1448, 2015.
9. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 580-587, 2014.
10. K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In arXiv prepring arXiv:1506. 01497, 2015.
11. L. Huang, Y. Yang, Y. Deng, and Y. Yu. Densebox: Unifying landmark localization with end to end object detection. arXiv preprint arXiv:1509. 04874, 2015.
12. V. Jain and E. G. Learned-Miller. Fddb: A benchmark for face detection in unconstrained settings. UMass Amherst Technical Report, 2010.
13. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the ACM International Conference on Multimedia, pages 675-678. ACM, 2014.
14. H. Jiang and E. Learned-Miller. Face detection with the faster r-cnn. arXiv preprint arXiv:1606. 03473, 2016.
15. K. H. J. S. Jifeng Dai, Yi Li. R-FCN: Object detection via region-based fully convolutional networks. arXiv preprint arXiv:1605. 06409, 2016.
16. Z. Kalal, J. Matas, and K. Mikolajczyk. Weighted sampling for large-scale boosting. In BMVC, pages 1-10, 2008.
17. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097-1105, 2012.
18. H. Li, Z. Lin, X. Shen, J. Brandt, and G. Hua. A convolutional neural network cascade for face detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5325-5334, 2015.
19. J. Li and Y. Zhang. Learning surf cascade for fast and accurate object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3468-3475, 2013.
20. Y. Li, B. Sun, T. Wu, and Y. Wang. Face Detection with End-to-End Integration of a ConvNet and a 3D Model, pages 420-436. Springer International Publishing, Cham, 2016.
21. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg. SSD: Single Shot MultiBox Detector, pages 21-37. Springer International Publishing, Cham, 2016.
22. J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3431-3440, 2015.
23. M. Mathias, R. Benenson, M. Pedersoli, and L. Van Gool. Face detection without bells and whistles. In Computer Vision-ECCV 2014, pages 720-735. Springer, 2014.
24. T. Ojala, M. Pietikainen, and T. Maenpaa. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on pattern analysis and machine intelligence, 24 (7):971-987, 2002.
25. H. Qin, J. Yan, X. Li, and X. Hu. Joint training of cascaded cnn for face detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3456-3465, 2016.
26. R. Ranjan, V. M. Patel, and R. Chellappa. Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. arXiv preprint arXiv:1603. 01249, 2016.
27. S. Ren, K. He, R. Girshick, and J. Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems, pages 91-99, 2015.
28. H. A. Rowley, S. Baluja, and T. Kanade. Neural networkbased face detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 20 (1):23-38, 1998.
29. X. Shen, Z. Lin, J. Brandt, and Y. Wu. Detecting and aligning faces by image retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3460-3467, 2013.
30. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409. 1556, 2014.
31. G. Trigeorgis, P. Snape, M. A. Nicolaou, E. Antonakos, and S. Zafeiriou. Mnemonic descent method: A recurrent process applied for end-to-end face alignment. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4177-4187, 2016.
32. J. R. Uijlings, K. E. van de Sande, T. Gevers, and A. W. Smeulders. Selective search for object recognition. International journal of computer vision, 104 (2):154-171, 2013.
33. P. Viola and M. J. Jones. Robust real-time face detection. International journal of computer vision, 57 (2):137-154, 2004.
34. S. Wan, Z. Chen, T. Zhang, B. Zhang, and K.-k. Wong. Bootstrapping face detection with hard negative examples. arXiv preprint arXiv:1608. 02236, 2016.
35. J. Yan, Z. Lei, L. Wen, and S. Li. The fastest deformable part model for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2497-2504, 2014.
36. J. Yan, X. Zhang, Z. Lei, and S. Z. Li. Real-time high performance deformable model for face detection in the wild. In Biometrics (ICB), 2013 International Conference on, pages 1-6. IEEE, 2013.
37. J. Yan, X. Zhang, Z. Lei, and S. Z. Li. Face detection by structural models. Image and Vision Computing, 32 (10):790-799, 2014.
38. Y. L. L. C. W. G. Yancheng Bai, Wenjing Ma and L. Yang. Multi-scale fully convolutional network for fast face detection. In Proceedings of the British Machine Vision Conference (BMVC). BMVA Press, September 2016.
39. B. Yang, J. Yan, Z. Lei, and S. Z. Li. Aggregate channel features for multi-view face detection. In Biometrics (IJCB), 2014 IEEE International Joint Conference on, pages 1-8. IEEE, 2014.
40. S. Yang, P. Luo, C.-C. Loy, and X. Tang. From facial parts responses to face detection: A deep learning approach. In Proceedings of the IEEE International Conference on Computer Vision, pages 3676-3684, 2015.
41. S. Yang, P. Luo, C. C. Loy, and X. Tang. Wider face: A face detection benchmark. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
42. J. Yu, Y. Jiang, Z. Wang, Z. Cao, and T. Huang. Unitbox: An advanced object detection network. In Proceedings of the 2016 ACM on Multimedia Conference, MM '16, pages 516-520, New York, NY, USA, 2016. ACM.
43. S. Zafeiriou, C. Zhang, and Z. Zhang. A survey on face detection in the wild: Past, present and future. Computer Vision and Image Understanding, 138:1-24, 2015.
44. S. Zagoruyko, A. Lerer, T.-Y. Lin, P. O. Pinheiro, S. Gross, S. Chintala, and P. Dollár. A multipath network for object detection. arXiv preprint arXiv:1604. 02135, 2016.
45. K. Zhang, Z. Zhang, Z. Li, and Y. Qiao. Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Processing Letters, 23 (10):1499-1503, Oct 2016.
46. L. Zhang, L. Lin, X. Liang, and K. He. Is Faster R-CNN Doing Well for Pedestrian Detection, pages 443-457. Springer International Publishing, Cham, 2016.
47. B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva. Learning deep features for scene recognition using places database. In Advances in neural information processing systems, pages 487-495, 2014.
48. C. Zhu, Y. Zheng, K. Luu, and M. Savvides. Cms-rcnn: Contextual multi-scale region-based cnn for unconstrained face detection. arXiv preprint arXiv:1606. 05413, 2016.
49. X. Zhu and D. Ramanan. Face detection, pose estimation, and landmark localization in the wild. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 2879-2886. IEEE, 2012.
50. C. L. Zitnick and P. Dollár. Edge boxes: Locating object proposals from edges. In European Conference on Computer Vision, pages 391-405. Springer, 2014.