YOLO-FD: YOLO for Face Detection-Reference-Cited by-同舟云学术

YOLO-FD: YOLO for Face Detection

Published:2019 Issue: Volume: Page:209-218
ISSN:0302-9743
Container-title:Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
language:
Short-container-title:

Author:

Silva Luan P. e^ORCID,Batista Júlio C.^ORCID,Bellon Olga R. P.^ORCID,Silva Luciano^ORCID

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-33904-3_19

Reference21 articles.

1. Batista, J.C., Albiero, V., Bellon, O.R., Silva, L.: AUMPNet: simultaneous action units detection and intensity estimation on multipose facial images using a single convolutional neural network. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition, FG 2017, pp. 866–871. IEEE (2017)

2. Günther, M., et al.: Unconstrained face detection and open-set face recognition challenge. In: 2017 IEEE International Joint Conference on Biometrics (IJCB), pp. 697–706. IEEE (2017)

3. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

4. Hu, P., Ramanan, D.: Finding tiny faces. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1522–1530. IEEE (2017)

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

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