A Combined GLQP and DBN-DRF for Face Recognition in Unconstrained Environments
Hongping Hu, Yu Yang
Available Online June 2017.
- https://doi.org/10.2991/caai-17.2017.124How to use a DOI?
- face recognition; local quantized patterns; gabor filters; deep belief networks; dynamic random forests
- This paper proposes a novel approach for accurate and robust face recognition by using Local Quantized Patterns computed from gabor-filtered images(GLQP) and Deep Belief Network ensembled dynamic random forests(DBN-DRF). GLQP is a kind of local pattern feature extractor based on gabor filters applying, it makes use of vector quantization and lookup table to let local features become more expressive without sacrificing simplicity and computational efficiency. DBN-DRF is a new deep architecture we proposed, in which dynamic random forests classifier is employed to replace inherent Softmax classifier or SVM to achieve a decent classification result at the top of network. GLQP exploits low-level local features that are used as input to DBN-DRF, which further extracts high-level abstract features for classification. Our architecture is trained and evaluated on two challenging face recognition datasets(FERET and LFW), the experiments result show our approach is competitive or better than the state of the arts.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Hongping Hu AU - Yu Yang PY - 2017/06 DA - 2017/06 TI - A Combined GLQP and DBN-DRF for Face Recognition in Unconstrained Environments BT - 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017) PB - Atlantis Press SP - 553 EP - 557 SN - 1951-6851 UR - https://doi.org/10.2991/caai-17.2017.124 DO - https://doi.org/10.2991/caai-17.2017.124 ID - Hu2017/06 ER -