publication . Preprint . 2017

Face Attention Network: An Effective Face Detector for the Occluded Faces

Wang, Jianfeng; Yuan, Ye; Yu, Gang;
Open Access English
  • Published: 20 Nov 2017
Abstract
The performance of face detection has been largely improved with the development of convolutional neural network. However, the occlusion issue due to mask and sunglasses, is still a challenging problem. The improvement on the recall of these occluded cases usually brings the risk of high false positives. In this paper, we present a novel face detector called Face Attention Network (FAN), which can significantly improve the recall of the face detection problem in the occluded case without compromising the speed. More specifically, we propose a new anchor-level attention, which will highlight the features from the face region. Integrated with our anchor assign str...
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition
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41 references, page 1 of 3

[1] D. Chen, G. Hua, F. Wen, and J. Sun. Supervised transformer network for efficient face detection. In European Conference on Computer Vision, pages 122-138. Springer, 2016.

[2] Y. Chen, L. Song, and R. He. Masquer hunter: Adversarial occlusion-aware face detection. arXiv preprint arXiv:1709.05188, 2017.

[3] 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.

[4] P. Dollar, R. Appel, S. J. Belongie, and P. Perona. Fast feature pyramids for object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(8):1532- 1545, 2014. [OpenAIRE]

[5] P. Felzenszwalb, D. McAllester, and D. Ramanan. A discriminatively trained, multiscale, deformable part model. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1-8, 2008. [OpenAIRE]

[6] S. Ge, J. Li, Q. Ye, and Z. Luo. Detecting masked faces in the wild with lle-cnns. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2682-2690, 2017.

[7] R. Girshick. Fast r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 1440-1448, 2015.

[8] Z. Hao, Y. Liu, H. Qin, J. Yan, X. Li, and X. Hu. Scale-aware face detection. arXiv preprint arXiv:1706.09876, 2017.

[9] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385, 2015.

[10] P. Hu and D. Ramanan. Finding tiny faces. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017.

[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] M. Jaderberg, K. Simonyan, A. Zisserman, et al. Spatial transformer networks. In Advances in Neural Information Processing Systems, pages 2017-2025, 2015. [OpenAIRE]

[13] H. Jiang and E. Learned-Miller. Face detection with the faster r-cnn. In Automatic Face & Gesture Recognition, pages 650-657. IEEE, 2017.

[14] 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.

[15] Y. Li, B. Sun, T. Wu, and Y. Wang. face detection with endto-end integration of a convnet and a 3d model. In European Conference on Computer Vision, pages 420-436. Springer, 2016.

41 references, page 1 of 3
Abstract
The performance of face detection has been largely improved with the development of convolutional neural network. However, the occlusion issue due to mask and sunglasses, is still a challenging problem. The improvement on the recall of these occluded cases usually brings the risk of high false positives. In this paper, we present a novel face detector called Face Attention Network (FAN), which can significantly improve the recall of the face detection problem in the occluded case without compromising the speed. More specifically, we propose a new anchor-level attention, which will highlight the features from the face region. Integrated with our anchor assign str...
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition
Download from
41 references, page 1 of 3

[1] D. Chen, G. Hua, F. Wen, and J. Sun. Supervised transformer network for efficient face detection. In European Conference on Computer Vision, pages 122-138. Springer, 2016.

[2] Y. Chen, L. Song, and R. He. Masquer hunter: Adversarial occlusion-aware face detection. arXiv preprint arXiv:1709.05188, 2017.

[3] 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.

[4] P. Dollar, R. Appel, S. J. Belongie, and P. Perona. Fast feature pyramids for object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(8):1532- 1545, 2014. [OpenAIRE]

[5] P. Felzenszwalb, D. McAllester, and D. Ramanan. A discriminatively trained, multiscale, deformable part model. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1-8, 2008. [OpenAIRE]

[6] S. Ge, J. Li, Q. Ye, and Z. Luo. Detecting masked faces in the wild with lle-cnns. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2682-2690, 2017.

[7] R. Girshick. Fast r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 1440-1448, 2015.

[8] Z. Hao, Y. Liu, H. Qin, J. Yan, X. Li, and X. Hu. Scale-aware face detection. arXiv preprint arXiv:1706.09876, 2017.

[9] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385, 2015.

[10] P. Hu and D. Ramanan. Finding tiny faces. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017.

[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] M. Jaderberg, K. Simonyan, A. Zisserman, et al. Spatial transformer networks. In Advances in Neural Information Processing Systems, pages 2017-2025, 2015. [OpenAIRE]

[13] H. Jiang and E. Learned-Miller. Face detection with the faster r-cnn. In Automatic Face & Gesture Recognition, pages 650-657. IEEE, 2017.

[14] 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.

[15] Y. Li, B. Sun, T. Wu, and Y. Wang. face detection with endto-end integration of a convnet and a 3d model. In European Conference on Computer Vision, pages 420-436. Springer, 2016.

41 references, page 1 of 3
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