**1. Introduction**

Biometric is a technique that uses inherent and unique biometric feature to recognize people identification [1]. Biometric authentication systems are well established today as they exhibit many advantages over traditional password and token-based ones [2]. Dorsal hand vein recognition mainly uses the subcutaneous vein tissue structure of the dorsal hand for personal identification, the vein structure of the back of hands is highlighted because of the different infrared light absorption rates [3]. Anatomical works [4] have proved that the structure of dorsal hand vein is unique in the process of growth and development. Therefore, research on the recognition of the dorsal hand vein is becoming more and more important in terms of value.

In recent years, more and more researchers have begun to pay attention to the algorithm of hand vein recognition. These algorithms for feature extraction are roughly divided into global and local texture features. The global texture feature, such as PCA(Principal components analysis, PCA) [5], it utilizes the geometric texture of the hand vein and the texture mapping of the ROI (region of interest), but to a certain extent, it ignores the local information which is separable. Its performance is easily affected by the change of viewing angle, illumination intensity, distortion and occlusion. Local texture feature, such as LBP(Local binary pattern, LBP) [6] and SIFT [7], pay attention to the relationship between key pixels and surrounding pixels, so the matching with local key features is more robust to the above-mentioned interference factors, Because the texture details of the hand vein is rather few, combining both a global and a local method was proposed and the performance has been improved. Zhang et al. proposed a Gaussian distribution based random key-point generation (GDRKG) [8] which

can obtain a reasonable number of key points with good coverage, so it could improve recognition performance. Wang et al. proposed cross-device hand vein recognition based on improved SIFT [9] which is based on the traditional SIFT, but optimized for the scale factor σ, using an extreme searching neighborhood structure and matching threshold R. It not only has had a significant improvement in the recognition rate in single-device experiments, but also a higher recognition rate than the traditional SIFT in cross-device experiments. Li et al. proposed hand dorsal vein recognition by matching using a width skeleton model, which uses the width skeleton model (WSM) [10] containing width and structural information. It makes full use of the global shape information, making the ability to characterize vein features stronger.

Although the above methods have achieved a high recognition rate, research on the dorsal hand vein are mostly based on a database acquired by a single device. Considering the diversity of imaging acquisition devices, as well as changes in environment and growth, hand vein recognition is very limited. At present, most published research papers are carried out under the strong constraints of controlled environment and user cooperation to achieve higher recognition accuracy. How to improve the cross-device hand vein recognition rate in the condition of seldom cooperation for users is the main problem solved in this paper. We propose a feature extraction method based on bit planes and block mutual information. The optimal bit plane was selected to overcome the influence of brightness and noise. The texture features of dorsal hand vein were described by a block method, and the optimal number of blocks was determined by the average entropy matrix of different blocks. Then the mutual information among different blocks was calculated as texture features by three kinds of mutual information calculation modes. Finally, the Euclidean distance classifier was used for classification recognition. The recognition rate of dorsal hand vein images under a cross-device increased to 93.33%.
