Hierarchical discriminant feature learning for cross-modal face recognition
| Autor: | Xiaolin Xu, Yi Jin, Yidong Li |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Boosting (machine learning)
Computer Networks and Communications Computer science business.industry 020207 software engineering Pattern recognition 02 engineering and technology Facial recognition system Modal Discriminant Discriminative model Hardware and Architecture 0202 electrical engineering electronic engineering information engineering Media Technology Artificial intelligence business Feature learning Software |
| Zdroj: | Multimedia Tools and Applications. 79:33483-33502 |
| ISSN: | 1573-7721 1380-7501 |
| Popis: | Heterogeneous Face Recognition (HFR) refers to the problem of recognizing faces across different visual domains and has attached great attention owing to its tremendous potential benefits in practical applications. In this paper, a novel feature learning approach named hierarchical discriminant feature learning (HDFL) has been proposed for HFR. Different from traditional feature learning based HFR approaches, the proposed HDFL aims to learn the most discriminative information via a two-layer hierarchical boosting network (HBN), where the hierarchical discriminative information can be exploited in the learned features and the appearance difference can be effectively reduced, simultaneously. Extensive experiments on three different heterogeneous face databases demonstrate that our approach consistently outperforms the state-of-the-art methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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