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.

中文翻译：

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联合判别式稀疏编码，用于基于手的鲁棒多模态识别

与单峰生物特征识别相比，多峰生物特征识别最近因其更高的安全性和有效性而引起了人们的极大兴趣。然而，大多数常规的多模式识别方法通常集中于独立地从不同的模式中提取语义信息，而忽略了跨模式之间的隐式关联。在本文中，我们提出了一种简单而有效的有监督的多峰特征学习方法，称为联合判别式稀疏编码（JDSC），该方法用于基于手的多峰识别，包括手指-静脉和手指-指印融合，手掌静脉和手指静脉的识别。掌纹融合，以及掌静脉和背手静脉融合。考虑到来自不同形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，类间的距离最大化，而类内的距离最小，同时，类内联运方式之间的相关性也最大化。 。因此，由获得的投影矩阵量化的稀疏二进制代码可以对多模式识别任务具有更大的判别能力。在六个常用的多峰数据集上进行的全面实验证明了我们提出的方法优于几种最新技术的优越性。由获得的投影矩阵量化的稀疏二进制代码可以对多模式识别任务具有更大的判别能力。在六个常用的多峰数据集上进行的全面实验证明了我们提出的方法优于几种最新技术的优越性。由获得的投影矩阵量化的稀疏二进制代码可以对多模式识别任务具有更大的判别能力。在六个常用的多峰数据集上进行的全面实验证明了我们提出的方法优于几种最新技术的优越性。