Joint Discriminative Sparse Coding for Robust Hand-Based Multimodal Recognition
Joint Discriminative Sparse Coding for Robust Hand-Based Multimodal Recognition
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DOI：
10.1109/tifs.2021.3074315
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发表时间：
2021-04
影响因子：
6.8
通讯作者：
Shuyi Li;Bob Zhang
中科院分区：
文献类型：
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作者：
Shuyi Li;Bob Zhang
与单峰生物特征识别相比，多峰生物特征识别具有更高的安全性和有效性，近年来引起了人们的极大兴趣。然而，大多数传统的多模式识别方法一般侧重于从不同的模式中独立地提取语义信息，而忽略了模式间的隐含相关性。本文提出了一种简单而有效的有监督的多模式特征学习方法，称为联合判别稀疏编码(JDSC)，用于手部多模式识别，包括手指-静脉和指节-指纹融合，手掌-静脉和掌纹融合，以及手掌-静脉和手背静脉融合。考虑到来自不同模式的相关样本具有语义相关性，JDSC将原始数据投影到一个共享空间中，在该共享空间中，类间距离最大化，类内距离最小化，同时类内各通道之间的相关性最大化。因此，由得到的投影矩阵量化的稀疏二进制码对多模式识别任务具有更强的区分能力。在六个常用的多模式数据集上的实验表明，我们提出的方法比几种最先进的技术具有更好的性能。
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.