Dorsal hand vein recognition, exploiting the biological information of human vein distribution structure, has the superiority of uniqueness, strong confidentiality and strong anti-forgery ability. The main challenges of the efficient dorsal hand vein recognition via usual reflection-type near infrared means are low resolution, big intra-class variation and insufficient samples. To address these issues, this paper proposed a novel dorsal hand vein recognition system based on the deep residual network with attention mechanism (DRNAM). Specifically, the improved dorsal hand vein imaging is designed by the transmission-type near infrared spectrum. Then, the system aims to extract compact and discriminative features from the dorsal hand vein image by the DRNAM model, which improves the robustness of feature extraction via cross channel and spatial information fusion. Finally, the method achieves the recognition result through the iterative training of the model. Briefly, the DRNAN model could effectively recognize the dorsal hand vein based on near infrared spectral imaging. The experimental results demonstrate that the dorsal hand vein image based on transmission-type near infrared spectrum is clearer than that based on reflection-type near infrared spectrum, and the proposed dorsal hand vein recognition method based on DRNAM outperforms the works based on the traditional convolutional neural network.

手背静脉识别，利用人体静脉分布结构的生物信息，具有唯一性、保密性强、防伪能力强等优势。通过通常的反射式近红外​​方法进行有效的手背静脉识别的主要挑战是分辨率低、类内变异大和样本不足。针对这些问题，本文提出了一种新的基于带有注意机制的深度残差网络（DRNAM）的手背静脉识别系统。具体来说，改进的手背静脉成像是通过透射型近红外光谱设计的。然后，该系统旨在通过 DRNAM 模型从手背静脉图像中提取紧凑和可区分的特征，通过跨通道和空间信息融合提高了特征提取的鲁棒性。最后，该方法通过模型的迭代训练达到识别结果。简而言之，DRANN 模型可以基于近红外光谱成像有效识别手背静脉。实验结果表明，基于透射型近红外光谱的手背静脉图像比基于反射型近红外光谱的手背静脉图像更清晰，提出的基于DRNAM的手背静脉识别方法优于基于传统卷积的方法。神经网络。基于近红外光谱成像的 DRANN 模型可以有效识别手背静脉。实验结果表明，基于透射型近红外光谱的手背静脉图像比基于反射型近红外光谱的手背静脉图像更清晰，提出的基于DRNAM的手背静脉识别方法优于基于传统卷积的方法。神经网络。基于近红外光谱成像的 DRANN 模型可以有效识别手背静脉。实验结果表明，基于透射型近红外光谱的手背静脉图像比基于反射型近红外光谱的手背静脉图像更清晰，提出的基于DRNAM的手背静脉识别方法优于基于传统卷积的方法。神经网络。