As people’s daily behavioral activities become more data-based, how to protect personal information security is a crucial consideration for the whole society. Finger vein recognition is becoming an essential means of identification because of its uniqueness, live detection, security, and many other advantages. Although deep learning can make finger vein recognition have an excellent effect. However, the number of samples needed to build a deep network model is too large, and the current authoritative finger vein database cannot reach the minimum number of samples required. The emergence of Muti-Grained Cascade Forest provides a solution to the problem of insufficient sample data and long training time, which can give a new research avenue in feature extraction. In order to obtain higher accuracy, the deep forest algorithm is introduced in this paper to process the finger vein images. Firstly, the image data in the finger vein image database is pre-processed to prepare for the subsequent feature extraction and matching. Then, the deep forest algorithm is used to find the feature points, and the ORB algorithm is used to match the features to obtain the angular information of each matched pair, and the final identity is determined according to the sparse distribution of angles. The accuracy of finger vein recognition based on the deep forest algorithm is 98.40%. By comparing with other machine learning methods for finger vein recognition, the proposed method has a higher accuracy rate.

中文翻译：

---

网络游戏QoS和QoE影响因素介绍


随着人们的日常行为活动更加数据化，如何保护个人信息安全成为全社会重要的考虑。指静脉识别因其唯一性、活体检测、安全性等诸多优势，正在成为重要的身份识别手段。虽然深度学习可以让指静脉识别有极好的效果。然而，构建深度网络模型所需的样本数量太大，目前权威的指静脉数据库无法达到所需的最小样本数量。多粒级联森林的出现解决了样本数据不足、训练时间长的问题，为特征提取提供了新的研究途径。为了获得更高的精度，本文引入深度森林算法对手指静脉图像进行处理。首先对指静脉图像数据库中的图像数据进行预处理，为后续的特征提取和匹配做好准备。然后利用深度森林算法寻找特征点，利用ORB算法进行特征匹配，得到每对匹配对的角度信息，根据角度的稀疏分布确定最终的身份。基于深度森林算法的指静脉识别准确率为98.40%。通过与其他用于指静脉识别的机器学习方法相比，该方法具有更高的准确率。