改进Stacking集成学习的指纹识别算法

针对传统卷积神经网络对多传感器指纹识别泛化能力降低、准确率不高的问题，提出改进的Stacking集成学习算法。首先将AlexNet进行改进，在AlexNet中引入深度可分离卷积减少参数量，加快训练速度；引入空间金字塔池化，提升网络获取全局信息的能力；引入批归一化，加快网络收敛速度,同时提升网络在测试集上的准确率；使用全局平均池化替代全连接层，防止过拟合。然后将DenseNet和改进的AlexNet 2种卷积神经网络作为Stacking的基学习器对指纹进行分类，获得预测结果。最后对相同基学习器训练得到的各个模型，根据预测精度对各预测结果赋权，得到的预测结果再由元分类器分类。改进的Stacking算法在多传感器指纹数据库上进行实验，最终识别准确率达98.43%，相对AlexNet提升了20.05%，相对DenseNet提升了4.25%。

A fingerprint recognition algorithm basedon improved Stacking ensemble learning

Aiming at the problem that the generalization ability of traditional convolutional neural network for multi-sensor fingerprint recognition is reduced and the accuracy is not high, an improved Stacking algorithm is proposed. Firstly, AlexNet is improved by introducing depth-separable convolution to reduce the number of parameters and speed up the training. The spatial pyramid pool is introduced to improve the ability of the network to obtain global information. Batch normalization is introduced to speed up network convergence and improve accuracy of the network on the test set. Global average pooling is used instead of fully connected layer to prevent overfitting. Then DenseNet and the improved AlexNet convolutional neural networks are used as the base learner of Stacking to classify fingerprints and obtain the prediction results. Finally, each model trained with the same base learner is weighted according to the prediction accuracy, and the prediction results are then classified by the meta-classifier. The improved Stacking algorithm is tested on multi-sensor fingerprint database, and the final recognition accuracy is 98.43%, which is 20.05% higher than AlexNet and 4.25% higher than DenseNet.