no code implementations • 20 Oct 2019 • Zelin Xiao, Hongxin Lin, Renjie Li, Hongyang Chao, Shengyong Ding
Interestingly, the principal component analysis exactly provides an effective way to define such a frame, i. e. setting the principal components as the frame axes.
no code implementations • 14 Aug 2019 • Hongxin Lin, Zelin Xiao, Yang Tan, Hongyang Chao, Shengyong Ding
Deep models are capable of fitting complex high dimensional functions while usually yielding large computation load.
no code implementations • 23 Oct 2018 • Yang Tan, Hongxin Lin, Zelin Xiao, Shengyong Ding, Hongyang Chao
However, such devices only provide sparse(limited speckles in structured light system) and noisy 3D data which can not support face recognition directly.
no code implementations • 24 Nov 2016 • Junyu Wu, Shengyong Ding, Wei Xu, Hongyang Chao
However, we observe that directly feeding the hallucinated facial images into recog- nition models can even degrade the recognition performance despite the much better visualization quality.
no code implementations • 24 Nov 2016 • Shengyong Ding, Junyu Wu, Wei Xu, Hongyang Chao
In this paper, we propose a method to automatically and incrementally construct datasets from massive weakly labeled data of the target domain which are readily available on the Internet under the help of a pretrained face model.
no code implementations • 15 Apr 2016 • Guangrun Wang, Liang Lin, Shengyong Ding, Ya Li, Qing Wang
The past decade has witnessed the rapid development of feature representation learning and distance metric learning, whereas the two steps are often discussed separately.
Ranked #7 on Person Re-Identification on SYSU-30k (using extra training data)
no code implementations • 11 Dec 2015 • Shengyong Ding, Liang Lin, Guangrun Wang, Hongyang Chao
Identifying the same individual across different scenes is an important yet difficult task in intelligent video surveillance.
Ranked #9 on Person Re-Identification on SYSU-30k (using extra training data)
no code implementations • 13 Jul 2015 • Zhujin Liang, Shengyong Ding, Liang Lin
This paper investigates how to rapidly and accurately localize facial landmarks in unconstrained, cluttered environments rather than in the well segmented face images.
no code implementations • 28 Jan 2015 • Liliang Zhang, Liang Lin, Xian Wu, Shengyong Ding, Lei Zhang
Sketch-based face recognition is an interesting task in vision and multimedia research, yet it is quite challenging due to the great difference between face photos and sketches.