The biometrics character has been widely used for individual identification and verification. Palmprint as one of biological features contains abundant discriminative features, which has already attracted a lot of interest. In this work, we focus on the identification and verification of contactless palmprint images. Considering the main differences between contact and contactless images, including orientation and deformation, we use a deep network combined with image alignment to further improve the recognition performance of contactless palmprint images. Recently, convolutional neural networks can well solve many classification problems, and researchers have proposed many networks with different architectures. We exploit the residual network in our framework, which achieves promising performance on the image classification problem. In order to improve the accuracy of verification, the spatial transformation network is used to align the image. The proposed method is tested on two public palmprint databases CASIA, GPDS. Extensive experiments are carried out with several state-of-the-art approaches as comparison, and the results demonstrated the effectiveness of our method.

中文翻译：

---

一种有效的非接触掌纹识别方法

生物特征已被广泛用于个人识别和验证。掌纹作为生物特征之一，包含丰富的判别特征，已经引起了广泛的关注。在这项工作中，我们专注于非接触式掌纹图像的识别和验证。考虑到接触和非接触图像之间的主要区别，包括方向和变形，我们使用深度网络结合图像对齐来进一步提高非接触掌纹图像的识别性能。最近，卷积神经网络可以很好地解决很多分类问题，研究人员提出了许多不同架构的网络。我们在我们的框架中利用了残差网络，这在图像分类问题上取得了有希望的表现。为了提高验证的准确性，使用空间变换网络来对齐图像。所提出的方法在两个公共掌纹数据库 CASIA、GPDS 上进行了测试。使用几种最先进的方法进行了广泛的实验作为比较，结果证明了我们方法的有效性。