Metric Learning Based Multi-Patch Ensemble for High Precision Face Verification
Dang-Dang Chen, Lan-Qing He, Zhong-Dao Wang, Sheng-Jin Wang
Available Online November 2016.
- https://doi.org/10.2991/ceis-16.2016.32How to use a DOI?
- face recognition; deep learning; CNN; LFW; metric learning
- Face verification under video surveillance is an important issue in computer vision for decades. Several methods on automatic face verification have significantly raised the accuracy by using Gabor wavelets, high-dimensional LBP features, Fisher vectors, Joint Bayesian, etc. Especially the usage of Deep Convolution Neural Networks(CNNs), artificial intelligence beats humans for the first time on Labeled Faces in the Wild(LFW) face verification task. In order to further improve the verification accuracy, we propose an approach that combines a multi-patch deep CNN by using two step metric learning. Experiments show that our method archives an accuracy of 99.37% on LFW, and 92.80% on YouTube Faces(YTF), which is very competitive with the state-of-the-art.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Dang-Dang Chen AU - Lan-Qing He AU - Zhong-Dao Wang AU - Sheng-Jin Wang PY - 2016/11 DA - 2016/11 TI - Metric Learning Based Multi-Patch Ensemble for High Precision Face Verification BT - Proceedings of the 2016 International Conference on Computer Engineering and Information Systems PB - Atlantis Press SP - 164 EP - 168 SN - 2352-538X UR - https://doi.org/10.2991/ceis-16.2016.32 DO - https://doi.org/10.2991/ceis-16.2016.32 ID - Chen2016/11 ER -