Joint deep convolutional feature representation for hyperspectral palmprint recognition
Joint deep convolutional feature representation for hyperspectral palmprint recognition
复制标题
高光谱掌纹识别的联合深度卷积特征表示
DOI：
10.1016/j.ins.2019.03.027
复制
发表时间：
2019-07-01
影响因子：
8.1
通讯作者：
Chen, C. L. Philip
中科院分区：
文献类型：
--
作者：
Zhao, Shuping;Zhang, Bob;Chen, C. L. Philip
高光谱图像分析具有多种光谱信息的特点，近年来受到越来越多的研究关注。这种增长也可以归因于计算机硬件的改进，这导致了卷积神经网络（CNN）的发展，在许多应用中实现了非常高的性能。基于这些技术，提出了一种联合深度卷积特征表示（JDCFR）方法用于高光谱掌纹识别。对于高光谱掌纹图像立方体，构建CNN堆栈以从整个光谱带中提取其特征并生成联合卷积特征。CNN堆栈包含数十个具有不同参数设置的CNN，这些CNN可以使用不同光谱中的掌纹图像进行本地训练。为了获得一个完整的和非冗余的特征集，并避免丢失隐藏在不同波段的层次特征，联合深度卷积特征是由一个基于协作表示的分类器（CRC）同时表示执行分类。实验结果是在由53个光谱波段和110，770张图像组成的高光谱掌纹数据集上进行的。与其他分类器、CNN、传统的掌纹识别方法以及对特征矩阵应用PCA相比，该方法获得了最高的性能，EER为0.01%，ARR为99.62%。(C)2019爱思唯尔公司All rights reserved.
With discriminative information from various spectrums, hyperspectral imaging analysis has recently attracted more and more considerable research attention. This increase can also be attributed to an improvement in computer hardware that has led to the development of Convolutional Neural Networks (CNNs) achieving very high performances in numerous applications. Motivated by these technologies, a Joint Deep Convolutional Feature Representation (JDCFR) methodology is proposed for hyperspectral palmprint recognition. For a hyperspectral palmprint image cube, a CNN stack is constructed to extract its features from the entire spectral bands and generate a joint convolutional feature. The CNN stack contains dozens of CNNs with different parameter settings, which can be trained locally using palmprint images in different spectrums. To obtain a complete and nonredundant set of features and to avoid losing hierarchical characteristics hidden in different bands, the Joint Deep Convolutional Feature is represented by a Collaborative Representation-based Classifier (CRC) simultaneously to perform classification. Experimental results were conducted on a hyperspectral palmprint dataset consisting of 53 spectral bands with 110,770 images. Compared with other classifiers, CNNs, traditional palmprint recognition methods, as well as applying PCA to the feature matrix, the proposed method achieved the highest performance with 0.01% - EER and 99.62% - ARR. (C) 2019 Elsevier Inc. All rights reserved.