针对智能手机面临的信息安全问题,研究了一种优化卷积深度信念网络的智能手机身份认证方法。先对采集的原始数据进行预处理,再引入稀疏自编码器进行预训练,预训练后的权重作为卷积深度信念网络模型的卷积核,选用逐层贪婪算法用于模型的正式训练;训练后,经均方根连接层对提取的特征进行整合,并利用监督学习算法调节均方根连接层与输出层之间的权重;最后,由Softmax分类器输出分类结果。该方法可直接处理高维手势数据,建立手势模型进行特征提取。仿真结果表明,与隐马尔科夫算法、深度信念网络算法相比,该方法可显著提高身份认证的准确率。

In this study, we propose an intelligent identity authentication method for an optimized convolutional deep belief network to address the information security problem faced by smart phones. First, the collected raw data is preprocessed and then input into the sparse autoencoder for pretraining. The pretrained weight is used as the convolution kernel of convolutional deep belief networks, and the layer-by-layer greedy algorithm is adopted to formally train the model. Subsequent to the training, the extracted features are integrated with the root mean square layer, and the weight between the root mean square layer and the output layer is adjusted using the supervised learning algorithm. Finally, the classification results are output through the Softmax classifier. The proposed method can directly process high-dimensional gesture data and establish a gesture model for feature extraction. Simulation results show that compared with the hidden Markov algorithm and the deep belief network algorithm, the proposed method can significantly improve the accuracy of identity authentication.