publication . Conference object . Other literature type . Preprint . 2015

Multi-view Face Detection Using Deep Convolutional Neural Networks

Farfade, Sachin Sudhakar; Saberian, Mohammad; Li, Li-Jia;
Open Access
  • Published: 09 Feb 2015
  • Publisher: ACM Press
Abstract
Comment: in International Conference on Multimedia Retrieval 2015 (ICMR)
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition, I.4
Related Organizations
42 references, page 1 of 3

[1] L. Bourdev and J. Brandt. Robust object detection via soft cascade. In Proceedings of CVPR, 2005. [OpenAIRE]

[2] P. Dollar, R. Appel, S. Belongie, and P. Perona. Fast feature pyramids for object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014.

[3] P. Dollar, Z. Tu, P. Perona, and S. Belongie. Integral channel features. In Proceedings of the British Machine Vision Conference, 2009.

[4] D. Erhan, C. Szegedy, A. Toshev, and D. Anguelov. Scalable object detection using deep neural networks. 2014.

[5] P. Felzenszwalb, D. McAllester, and D. Ramanan. A discriminatively trained, multiscale, deformable part model. In Proceedings of CVPR, 2008. [OpenAIRE]

[6] C. Garcia and M. Delakis. A Neural Architecture for Fast and Robust Face Detection. In Proceedings of IEEE-IAPR International Conference on Pattern Recognition, Aug. 2002. [OpenAIRE]

[7] C. Garcia and M. Delakis. Training Convolutional Filters for Robust Face Detection. In Proceedings of IEEE International Workshop of Neural Networks for Signal Processing, Sept. 2003.

[8] C. Garcia and M. Delakis. Convolutional face nder: a neural architecture for fast and robust face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004. [OpenAIRE]

[9] R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of CVPR, 2014.

[10] R. B. Girshick, F. N. Iandola, T. Darrell, and J. Malik. Deformable part models are convolutional neural networks. CoRR, 2014.

[11] P. E. Hadjidoukas, V. V. Dimakopoulos, M. Delakis, and C. Garcia. A high-performance face detection system using openmp. Concurrency and Computation: Practice and Experience, 2009. [OpenAIRE]

[12] C. Huang, H. Ai, Y. Li, and S. Lao. Vector boosting for rotation invariant multi-view face detection. In Proceedings of ICCV, 2005.

[13] C. Huang, H. Ai, Y. Li, and S. Lao. High-performance rotation invariant multiview face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007.

[14] F. Iandola, M. Moskewicz, S. Karayev, R. Girshick, T. Darrell, and K. Keutzer. Densenet: Implementing e cient convnet descriptor pyramids. arXiv preprint arXiv:1404.1869, 2014.

[15] V. Jain and E. Learned-Miller. Fddb: A benchmark for face detection in unconstrained settings. Technical Report UM-CS-2010-009, University of Massachusetts, Amherst, 2010.

42 references, page 1 of 3
Abstract
Comment: in International Conference on Multimedia Retrieval 2015 (ICMR)
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition, I.4
Related Organizations
42 references, page 1 of 3

[1] L. Bourdev and J. Brandt. Robust object detection via soft cascade. In Proceedings of CVPR, 2005. [OpenAIRE]

[2] P. Dollar, R. Appel, S. Belongie, and P. Perona. Fast feature pyramids for object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014.

[3] P. Dollar, Z. Tu, P. Perona, and S. Belongie. Integral channel features. In Proceedings of the British Machine Vision Conference, 2009.

[4] D. Erhan, C. Szegedy, A. Toshev, and D. Anguelov. Scalable object detection using deep neural networks. 2014.

[5] P. Felzenszwalb, D. McAllester, and D. Ramanan. A discriminatively trained, multiscale, deformable part model. In Proceedings of CVPR, 2008. [OpenAIRE]

[6] C. Garcia and M. Delakis. A Neural Architecture for Fast and Robust Face Detection. In Proceedings of IEEE-IAPR International Conference on Pattern Recognition, Aug. 2002. [OpenAIRE]

[7] C. Garcia and M. Delakis. Training Convolutional Filters for Robust Face Detection. In Proceedings of IEEE International Workshop of Neural Networks for Signal Processing, Sept. 2003.

[8] C. Garcia and M. Delakis. Convolutional face nder: a neural architecture for fast and robust face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004. [OpenAIRE]

[9] R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of CVPR, 2014.

[10] R. B. Girshick, F. N. Iandola, T. Darrell, and J. Malik. Deformable part models are convolutional neural networks. CoRR, 2014.

[11] P. E. Hadjidoukas, V. V. Dimakopoulos, M. Delakis, and C. Garcia. A high-performance face detection system using openmp. Concurrency and Computation: Practice and Experience, 2009. [OpenAIRE]

[12] C. Huang, H. Ai, Y. Li, and S. Lao. Vector boosting for rotation invariant multi-view face detection. In Proceedings of ICCV, 2005.

[13] C. Huang, H. Ai, Y. Li, and S. Lao. High-performance rotation invariant multiview face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007.

[14] F. Iandola, M. Moskewicz, S. Karayev, R. Girshick, T. Darrell, and K. Keutzer. Densenet: Implementing e cient convnet descriptor pyramids. arXiv preprint arXiv:1404.1869, 2014.

[15] V. Jain and E. Learned-Miller. Fddb: A benchmark for face detection in unconstrained settings. Technical Report UM-CS-2010-009, University of Massachusetts, Amherst, 2010.

42 references, page 1 of 3
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