Despite being highly secure, vein recognition suffers from the high inter-class similarity and intra-class variation resulting from the uncontrolled image capture, making the design of discriminative and robust representation very important. The recent success of convolutional neural network (CNN) for various image understanding tasks makes it a promising method for feature extraction. However, limited variability in small-scale datasets leads to systems derived from the direct training or fine-tuning not transferable and unreliable for practical biometric applications. This motivates the design of a multi-weighted co-occurrence descriptor encoding (MWCDE) model for vein recognition. Instead of directly conducting a feed-forward operation with a pre-trained CNN for obtaining the semantic features from the fully connected layers, co-occurrence features among convolutional filters are modeled first in MWCDE by a simple convolution between an indicator filter in a higher layer with a to-be-reweighted filter in a lower layer, and a redundancy-driven indicator filter selection algorithm is designed for filtering out some ambiguous representations. Second, another hard feature weighting strategy with a binary masking scheme is proposed for discarding noisy background and feature redundancy. The selected high-order descriptors are then embedded and aggregated into the compact feature vectors with a saliency driven spatial weighted Fisher vector algorithm, followed by the introduction of a generalized support vector machine for recognition. Extensive experiments with three benchmark vein datasets demonstrate that the proposed framework can achieve state-of-the-art results, and an additional experiment with the PolyU multispectral palmprint database illustrates its generalization ability. Code is available at (https://github.com/RobinCSIRO/MWCDE-for-Vein-Recognition).

中文翻译：

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静脉识别的多重加权共现描述符编码

尽管具有很高的安全性，但由于不受控制的图像捕获，静脉识别仍具有较高的类间相似性和类内变异，这使得区分性和鲁棒性表示的设计非常重要。卷积神经网络（CNN）在各种图像理解任务方面的最新成功使其成为一种有前途的特征提取方法。但是，小规模数据集中的有限可变性导致源自直接训练或微调的系统对于实际的生物统计应用而言是不可转让且不可靠的。这激发了用于静脉识别的多加权共现描述符编码（MWCDE）模型的设计。与其直接使用经过预训练的CNN进行前馈操作以从完全连接的层中获取语义特征，不如说是，首先在MWCDE中对卷积滤波器之间的共现特征进行建模，方法是在较高层的指标滤波器与较低层的待加权滤波器之间进行简单的卷积，并设计冗余驱动的指标滤波器选择算法进行滤波排除一些模棱两可的表示。其次，提出了另一种具有二进制掩蔽方案的硬特征加权策略，用于丢弃嘈杂的背景和特征冗余。然后，使用显着性驱动的空间加权Fisher向量算法将选定的高阶描述符嵌入并聚合到紧凑特征向量中，然后引入用于识别的通用支持向量机。使用三个基准静脉数据集进行的大量实验表明，该框架可以实现最新的结果，PolyU多光谱掌纹数据库的另一项实验说明了其泛化能力。可以在（https://github.com/RobinCSIRO/MWCDE-for-Vein-Recognition）上找到代码。