The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
An All-In-One Convolutional Neural Network for Face Analysis
Abstract
We present a multi-purpose algorithm for simultaneous face detection, face alignment, pose estimation, gender recognition, smile detection, age estimation and face recognition using a single deep convolutional neural network (CNN). Theproposed method employs a multi-task learning framework that regularizes the shared parameters of CNN and builds a synergy among different domains and tasks. Extensive experiments show that the network has a better understanding of face and achieves state-of-the-art result for most of these tasks.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839
FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267
(2016) · Cited by 11,679
Deep Learning Face Attributes in the Wild
Liu, Z., Luo, P., Wang, X., & Tang, X. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.425 · Cited by 7,747
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542
Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015) · Procedings of the British Machine Vision Conference 2015 · doi:10.5244/c.29.41 · Cited by 5,479
Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments
Huang, G. B., Mattar, M., Berg, T. L., & Miller, E. G. (2008) · HAL · Cited by 4,445
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. J. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5093 · Cited by 3,752
One millisecond face alignment with an ensemble of regression trees
Kazemi, V., & Sullivan, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.241 · Cited by 2,831
Cited by
Deep Facial Expression Recognition: A Survey
Li, S., & Deng, W. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.2981446 · Cited by 1,753
Fine-Grained Head Pose Estimation Without Keypoints
Ruiz, N., Chong, E., & Rehg, J. M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00281 · Cited by 631
Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms
Phillips, P. J., Yates, A. N., Hu, Y., Hahn, C. A., Noyes, E., Jackson, K., Cavazos, J. G., Jeckeln, G., Ranjan, R., Sankaranarayanan, S., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1721355115 · Cited by 461
Self-citation
Metadata
- Authors
- Ranjan, R., Sankaranarayanan, S., Castillo, C. D., & Chellappa, R.
- Venue
- 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)
- Publisher
- IEEE
- Published
- 2017-05
- Pages
- 17-24
- ISSN
- 1541-5058 (ISSN-L)
- OpenAlex
- W2548780814
- Cited by
- 409
- References
- 55
- FWCI
- 17.96
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Ranjan, R., Sankaranarayanan, S., Castillo, C. D., & Chellappa, R. (2017). An All-In-One Convolutional Neural Network for Face Analysis. 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017). https://doi.org/10.1109/fg.2017.137
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
