Name
Title | ||
|---|---|---|
Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition |
Abstract | ||
|---|---|---|
One key challenge of facial trait recognition is the large non-rigid appearance variations due to some irrelevant real world factors, such as viewpoint and expression changes. In this paper, we explore how the shape information, i.e. facial landmark positions, can be explicitly deployed into the popular Convolutional Neural Network (CNN) architecture to disentangle such irrelevant non-rigid appearance variations. First, instead of using fixed kernels, we propose a kernel adaptation method to dynamically determine the convolutional kernels according to the spatial distribution of facial landmarks, which helps learning more robust features. Second, motivated by the intuition that different local facial regions may demand different adaptation functions, we further propose a tree-structured convolutional architecture to hierarchically fuse multiple local adaptive CNN subnetworks. Comprehensive experiments on WebFace, Morph II and MultiPIE databases well validate the effectiveness of the proposed kernel adaptation method and tree-structured convolutional architecture for facial trait recognition tasks, including identity, age and gender recognition. For all the tasks, the proposed architecture consistently achieves the state-of-the-art performances. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/CVPR.2015.7298618 | 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) |
Keywords | Field | DocType |
shape driven kernel adaptation,convolutional neural network,robust facial trait recognition,nonrigid appearance variations,shape information,facial landmark positions,CNN architecture,tree-structured convolutional architecture,WebFace databases,Morph II databases,MultiPIE databases | Kernel (linear algebra),Computer vision,Architecture,Pattern recognition,Trait,Convolutional neural network,Computer science,Intuition,Artificial intelligence,Fuse (electrical),Landmark | Conference |
Volume | Issue | ISSN |
2015 | 1 | 1063-6919 |
Citations | PageRank | References |
20 | 0.59 | 33 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shaoxin Li | 1 | 282 | 13.39 |
| Junliang Xing | 2 | 1193 | 63.31 |
| Zhiheng Niu | 3 | 52 | 3.50 |
| Shiguang Shan | 4 | 6322 | 283.75 |
| Shuicheng Yan | 5 | 9701 | 359.54 |