Cross-Modality Feature Learning Through Generic Hierarchical Hyperlingual-Words

Loading...
Thumbnail Image
Authors
Shao, Ming
Fu, Yun
Subjects
Advisors
Date of Issue
2017
Date
Publisher
Language
Abstract
Recognizing facial images captured under visible light has long been discussed in the past decades. However, there are many impact factors that hinder its successful application in real-world, e.g., illumination, pose variations. Recent work has concentrated on different spectrals, i.e., near infrared, that can only be perceived by specifically designed device to avoid the illumination problem. However, this inevitably introduces a new problem, namely, cross-modality classification. In brief, images registered in the system are in one modality, while images that captured momentarily used as the tests are in another modality. In addition, there could be many within-modality variations— pose and expression—leading to a more complicated problem for the researchers. To address this problem, we propose a novel framework called hierarchical hyperlingual-words (Hwords) in this paper. First, we design a novel structure, called generic Hwords, to capture the high-level semantics across different modalities and within each modality in weakly supervised fash- ion, meaning only modality pair and variations information are needed in the training. Second, to improve the discriminative power of Hwords, we propose a novel distance metric through the hierarchical structure of Hwords. Extensive experiments on multimodality face databases demonstrate the superiority of our method compared with the state-of-the-art works on face recognition tasks subject to pose and expression variations.
Type
Article
Description
IEEE Transactions on Neural Networks and Learning Systems
The article of record as published may be found at http://dx.doi.org/10.1109/tnnls.2016.2517014
Department
Organization
Identifiers
NPS Report Number
Sponsors
Funded by Naval Postgraduate School
Funder
Naval Postgraduate School (Award N00244-15-1-0041)
Format
Citation
Distribution Statement
Rights
Copyright is reserved by the copyright owner.
Collections