Face Detection Algorithm Based on Convolutional Pooling Deep Belief Network
Dandan Wang, Ming Li, Xiaoxu Li
Available Online April 2017.
- 10.2991/eame-17.2017.64How to use a DOI?
- face detection; deep learning; CPDBN; partial occlusion
When using a single deep model to solve the problem of face detection, it is easy to have the problem of high false detection rate and low learning efficiency, the mixed model algorithm based on deep learning was proposed to solve these problems of face detection, which is called the CPDBN (Convolutional pooling deep belief network). Experimental results show that the algorithm improves the accuracy of face detection in the face of partial occlusion, and increases the robustness of multi-pose.
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Dandan Wang AU - Ming Li AU - Xiaoxu Li PY - 2017/04 DA - 2017/04 TI - Face Detection Algorithm Based on Convolutional Pooling Deep Belief Network BT - Proceedings of the 2017 2nd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2017) PB - Atlantis Press SP - 273 EP - 276 SN - 2352-5401 UR - https://doi.org/10.2991/eame-17.2017.64 DO - 10.2991/eame-17.2017.64 ID - Wang2017/04 ER -