手掌静脉识别:基于端到端卷积神经网络方法

Palm vein recognition based on end-to-end convolutional neural network

目的提出一种基于端到端卷积神经网络的手掌静脉识别方法。方法在构建的手掌静脉识别网络模型中,卷积层和池化层交替级联提取图像特征,同时通过神经网络分类器进行分类识别,采用包含动量项的随机梯度下降法最小化识别误差,在误差减小的方向上不断优化模型。采用训练集数据扩展、批归一化、Dropout、L2参数正则化四种方法提升网络的泛化能力。结果对公共的PolyU库(图像在高约束条件下获取)和自建库(图像在自然条件下获取)中全部500个对象的识别,正确识别率分别达到99.90%和98.05%,单个样本的识别时间均小于9 ms。结论与传统算法相比,本文方法能够有效提升掌静脉识别在实际应用中的准确率,为掌静脉识别提供一种新思路。

Objective We propose a novel palm-vein recognition model based on the end-to-end convolutional neural network. In this model, the convolutional layer and the pooling layer were alternately connected to extract the image features, and the categorical attribute was estimated simultaneously via the neural network classifier. The classification error was minimized via the mini-batch stochastic gradient descent algorithm with momentum to optimize the feature descriptor along with the direction of the gradient descent. Four strategies including data augmentation, batch normalization, dropout, and L^2 parameter regularization were applied in the model to reduce the generalization error. The experimental results showed that for classifying 500 subjects form PolyU database and a self-established database, this model achieved identification rates of 99.90% and 98.05%, respectively, with an identification time for a single sample less than 9 ms. The proposed approach, as compared with the traditional method, could improve the accuracy of palm vein recognition in clincal applications and provides a new approach to palm vein recognition.