MATEC Web Conf.
Volume 128, 20172017 International Conference on Electronic Information Technology and Computer Engineering (EITCE 2017)
|Number of page(s)||4|
|Section||Simulation Model and Algorithm|
|Published online||25 October 2017|
- Parkhi O M, Vedaldi A, Zisserman A. Deep Face Recognition[C]// British Machine Vision Conference. 2015:41.1–41.12. [Google Scholar]
- Taigman Y, Yang M, Ranzato M, et al. DeepFace: Closing the Gap to Human-Level Performance in Face Verification[C]// Computer Vision and Pattern Recognition. IEEE, 2014:1701–1708. [Google Scholar]
- Schroff F, Kalenichenko D, Philbin J. FaceNet: A Unified Embedding for Face Recognition and Clustering[C]// Computer Vision and Pattern Recognition. IEEE, 2015:815–823. [Google Scholar]
- Frenay B, Verleysen M. Classification in the presence of label noise: a survey[J]. IEEE Transactions on Neural Networks & Learning Systems, 2014, 25(5):845–869. [CrossRef] [Google Scholar]
- Fitzgibbon A W, Zisserman A. Joint manifold distance: a new approach to appearance based clustering[A].Fitzgibbon A W. IEEE Conference on Computer Vision and Pattern Recognition[C]. Madison: IEEE, 2003. [Google Scholar]
- Wu B, Zhang Y, Hu B G, et al. Constrained Clustering and Its Application to Face Clustering in Videos[C]// IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2013:3507–3514. [Google Scholar]
- Zhang S C, Fang B, Liang Y Z, et al. A face clustering method based on facial shape information[C]// International Conference on Wavelet Analysis and Pattern Recognition. IEEE, 2011. [Google Scholar]
- Hu Y, Dong Y. Face clustering using high-level feature based on deep learning[J]. 2015. [Google Scholar]
- STEINBACH M, KARYPIS G, KUMAR V. A comparison of document clustering techniques[C]// Proc of KDD-2000 Workshop on Text Mining. 2000. [Google Scholar]
- Guo Y, Zhang L, Hu Y, et al. MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition[C]// European Conference on Computer Vision. Springer, Cham, 2016:87–102. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.