Weighted Feature-Level Fusion of Color Local Texture Features for Face Recognition
Keywords:
Color face recognition (FR), color local texture features, color spaces, feature combination, local binary pattern (LBP)Abstract
This paper presents an extension of the
framework proposed by Choi et al. that uses color local texture features (CLTF) for face recognition. The original framework combines multiple CLTFs, each of which corresponds to the associated color channel, with no weights at feature-level. In the proposed extension, the combination is performed with weights that are calculated based on the contribution of each
channel to recognition performance. After the combination, PCA is used to select in the combining result the most important components which are used as the unique feature vector representing a face image. Comparative experiments have been conducted to evaluate the customized framework for FR on three public face databases, i.e., CMU-PIE, Color FERET and Postech Face's 01. Experimental results show that
the customized framework yields better recognition rates than the framework by Choi et al.
Downloads
Downloads
Published
Issue
Section
License
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
