Joint Discriminative Sparse Coding for Robust Hand-Based Multimodal Recognition

Multimodal biometrics recognition has recently attracted much interest for its higher security and effectiveness compared with unimodal biometrics recognition. However, most of the conventional multimodal recognition approaches generally focus on extracting semantic information from different modali...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on information forensics and security Vol. 16; pp. 3186 - 3198
Main Authors: Li, Shuyi, Zhang, Bob
Format: Journal Article
Language:English
Published: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Binary codes
Biometric recognition systems
Biometrics
Coding
Correlation
Encoding
Face recognition
Feature extraction
feature learning
hand-based
Image recognition
inter-modality
Multimodal biometrics
Palm
Semantics
sparse binary code
Sparse matrices
ISSN:1556-6013, 1556-6021
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Multimodal biometrics recognition has recently attracted much interest for its higher security and effectiveness compared with unimodal biometrics recognition. However, most of the conventional multimodal recognition approaches generally focus on extracting semantic information from different modalities independently, while ignoring the implicit correlations among inter-modality. In this paper, we propose a simple yet effective supervised multimodal feature learning method, called joint discriminative sparse coding (JDSC), which is applied for hand-based multimodal recognition including finger-vein and finger-knuckle-print fusion, palm-vein and palmprint fusion, as well as palm-vein and dorsal-hand-vein fusion. Considering that relevant samples from different modalities have semantic correlations, JDSC projects the raw data into a shared space in which the distance of the between-class is maximized and the distance of the within-class is minimized, at the same time, the correlation among the inter-modality of the within-class is maximized. Therefore, sparse binary codes quantified by the obtained projection matrix can have more discriminative power for multimodal recognition tasks. Thorough experiments on six commonly used multimodal datasets demonstrate the superiority of our proposed method over several state-of-the-art techniques.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1556-6013
1556-6021
DOI:10.1109/TIFS.2021.3074315