Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Palmprint Recognition <s> Deep learning method has been considered as a breakthrough in computer vision, successfully aplied in many domains, including biometrics. Palmprint recognition has been accepted with high acceptability and low intrusion. In this study, deep learning was introduced into palmprint recognition for a better performance. Three concrete steps were involved in the application. First, a deep belief net was built by top-to-down unsupervised training with training samples. Second, the optimum parameters were chosen to adapt the model for a robust performance. Third, the testing samples were labeled by employing the deep learning models. Compared with traditional recognition methods, such as PCA, LBP, the experimental results show that deep learning method has a higher recognition rate for palmprint recognition. <s> BIB001 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Palmprint Recognition <s> Recently, palmprint representation using different descriptors under the incorporation of deep neural networks, always achieves significant recognition performance. In this paper, we proposed a novel method to achieve end-to-end palmprint recognition by using Siamese network. In our network, two parameter-sharing VGG-16 networks were employed to extract two input palmprint images’ convolutional features, and the top network directly obtained the similarity of two input palmprints according to their convolutional features. This method had a good performance on PolyU dataset and achieved a high recognition outcome with an Equal Error Rate (EER) of 0.2819%. To test the robustness of the proposed algorithm, we collected a palmprint dataset called XJTU from the practical daily environment. On XJTU, the EER of our method is 4.559%, which highlighted a promising potential of the usage of palmprint in personal identification system. <s> BIB002 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Palmprint Recognition <s> Palmprint gender classification can revolutionise the performance of authentication systems, reduce searching space and speed up matching rate. However, to the best of their knowledge, there is no literature addressing this issue. The authors design a new convolutional neural network (CNN) structure, fine-tuning Visual Geometry Group Network, up to 19 layers to achieve a 20-layer network, for palmprint gender classification. Experimental results show that the proposed structure could achieve good performance for gender classification. They also investigate palmprint images with 15 different kinds of spectra. They empirically find that a palmprint image acquired by the Blue spectrum could achieve 89.2% correct classification and could be considered as a suitable spectrum for gender classification. The neural network is able to classify a 224 × 224 × 3-pixel palmprint image in <23 ms, verifying that the proposed CNN is an effective real-time solution. <s> BIB003 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Palmprint Recognition <s> At present, palmprint recognition based on deep learning has been more and more widely used in identity recognition due to its many advantages. However, these algorithms often require a large amount of labelled data for training. In fact, it is difficult and expensive to get enough data that meets the requirements. In this Letter, based on a small amount of labelled images, the authors proposed a method for few-shot palmprint recognition using Graph Neural Networks (GNNs). The palmprint features extracted by the convolutional neural network are processed into nodes in the GNN. The edges in the GNN are used to represent similarities between image nodes. The parameters in the network are continuously optimised, and finally, the category to which each image belongs is obtained. Further, they adopted a mobile phone to create a palmprint database in an unconstrained way. Adequate experiments were performed on the benchmark database and the authors' self-built database. The experimental results show that their proposed GNN-based few-shot palmprint recognition can obtain state-of-the-art performance, where the accuracy is over 99.90%. <s> BIB004 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Palmprint Recognition <s> Recently, excellent palmprint recognition algorithms have emerged and achieved satisfactory performance. However, cross-domain palmprint recognition is rarely considered. In this paper, we proposed transfer autoencoder for crossdomain palmprint recognition. Convolutional autoencoders were firstly used to extract low-dimensional features. A discriminator was then introduced to reduce the gap of two domains. The autoencoders and discriminator were alternately trained, and finally the features with the same distribution were extracted. The databases collected from different environments are defined as source and target domains, respectively. Based on the labels in source domain, unsupervised identification of target domain can be achieved. The experiments were performed on 24 cross-domain pairs composed by multispectral database and our self-built uncontrolled databases. The results show that transfer autoencoder can greatly improve cross-domain recognition accuracy, up to 23.26%. At the same time, the accuracy in a single domain can reach over 99% in controlled database. <s> BIB005 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Palmprint Recognition <s> 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. <s> BIB006
In BIB001 , Xin et al. proposed one of the early works on palmprint recognition using a deep learning framework. The authors built a deep belief net by top-todown unsupervised training, and tuned the model parameters toward a robust accuracy on the validation set. Their experimental analysis showed a performance gain over classical models that are based on LBP, and PCA, and other other hand-crafted features. In another work, Samai et al. proposed a deep learningbased model for 2D and 3D palmprint recognition  . They proposed an efficient biometric identification sys-tem combining 2D and 3D palmprint by fusing them at matching score level. To exploit the 3D palmprint data, they converted them to grayscale images by using the Mean Curvature (MC) and the Gauss Curvature (GC). They then extracted features from images using Discrete Cosine Transform Net (DCT Net). Zhong et al. proposed a palmprint recognition algorithm using Siamese network BIB002 . Two VGG-16 networks (with shared parameters) were employed to extract features for two input palmprint images, and another network is used on top of them to directly obtain the similarity of two input palmprints according to their convolutional features. This method achieved an Equal Error Rate (EER) of 0.2819% on on PolyU dataset. In  , Izadpanahkakhk et al. proposed a transfer learning approach towards palmprint verification, which jointly extracts regions of interests and features from the images. They use a pre-trained convolutional network, along with SVM to make prediction. They achieved an IoU score of 93% and EER of 0.0125 on Hong Kong Polytechnic University Palmprint (HKPU) database. In BIB004 , Shao and Zhong proposed a few-shot palmprint recognition model using a graph neural network. In this work, the palmprint features extracted by a convolutional neural network are processed into nodes in the GNN. The edges in the GNN are used to represent similarities between image nodes. In a more recent work  , Shao and colleagues proposed a deep palmprint recognition approach by combining hash coding and knowledge distillation. Deep hashing network are used to convert palmprint images to binary codes to save storage space and speed up the matching process. The architecture of the proposed deep hashing network is shown in Figure 18 . They also proposed a database for unconstrained palmprint recognition, which consists of more than 30,000 images collected by 5 different mobile phones, and achieved promising results on that dataset. In BIB005 , Shao et al. proposed a cross-domain palmprint recognition based on transfer convolutional autoencoder. Convolutional autoencoders were firstly used to extract low-dimensional features. A discriminator was then introduced to reduce the gap of two domains. The auto-encoders and discriminator were alternately trained, and finally the features with the same distribution were extracted. In BIB006 , Zhao and colleagues proposed a joint deep convolutional feature representation for hyperspectral palmprint recognition. A CNN stack is constructed to extract its features from the entire spectral bands and generate a joint convolutional feature. They evaluated their model on a hyperspectral palmprint dataset consisting of 53 spectral bands with 110,770 images. They achieved an EER of 0.01%. In BIB003 , Xie et al. proposed a gender classification framework using convolutional neural network on plamprint images. They finetuned the pre-trained VGGNet on a palmprint dataset and showed that the proposed structure could achieve a good performance for gender classification.