Efficient Re-Clustering for Convolutional Neural Networks
Cheng-Ying Wang, Feng Cen
Available Online November 2016.
- https://doi.org/10.2991/ceis-16.2016.22How to use a DOI?
- face recognition; t-distribution; convolutional network; re-clustering; dimensional reduction
- With the rapid development of face recognition, it has been widely applied to various fields and scenes. Due to the unstable light condition, rotation and occlusion, the stability of the recognition performance is still an issue. In this work, we study the VGG model and analyze features' distribution extracted with the VGG model. To achieving good performance, we suggest a t-distribution based VGG model to reduce the dimension of features and re-clustering them. Furthermore, by recording the path of dimension reduction in training phase, the computational complexity of the test phase is decreased. The proposed algorithm is evaluated on public available datasets. The experimental results demonstrate the significant improvement of the proposed approach on both the simplification of feature space and the efficiency of recognition, especially on limited data.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Cheng-Ying Wang AU - Feng Cen PY - 2016/11 DA - 2016/11 TI - Efficient Re-Clustering for Convolutional Neural Networks BT - Proceedings of the 2016 International Conference on Computer Engineering and Information Systems PB - Atlantis Press SP - 111 EP - 116 SN - 2352-538X UR - https://doi.org/10.2991/ceis-16.2016.22 DO - https://doi.org/10.2991/ceis-16.2016.22 ID - Wang2016/11 ER -