We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
NIRExpNet: Three-Stream 3D Convolutional Neural Network for Near Infrared Facial Expression Recognition.
- Authors
Zhan Wu; Tong Chen; Ying Chen; Zhihao Zhang; Guangyuan Liu
- Abstract
Facial expression recognition (FER) under active near-infrared (NIR) illumination has the advantages of illumination invariance. In this paper, we propose a three-stream 3D convolutional neural network, named as NIRExpNet for NIR FER. The 3D structure of NIRExpNet makes it possible to extract automatically, not just spatial features, but also, temporal features. The design of multiple streams of the NIRExpNet enables it to fuse local and global facial expression features. To avoid over-fitting, the NIRExpNet has a moderate size to suit the Oulu-CASIA NIR facial expression database that is a medium-size database. Experimental results show that the proposed NIRExpNet outperforms some previous state-of-art methods, such as Histogram of Oriented Gradient to 3D (HOG 3D), Local binary patterns from three orthogonal planes (LBP-TOP), deep temporal appearance-geometry network (DTAGN), and adapt 3D Convolutional Neural Networks (3D CNN DAP).
- Subjects
HUMAN facial recognition software; NEAR infrared radiation; ARTIFICIAL neural networks
- Publication
Applied Sciences (2076-3417), 2017, Vol 7, Issue 11, p1184
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app7111184