Joint deep convolutional feature representation for hyperspectral palmprint recognition

Abstract 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.