Palmprint Recognition Method Research Articles

Structure Suture Learning-Based Robust Multiview Palmprint Recognition.

Low-quality palmprint images will degrade the recognition performance, when they are captured under the open, unconstraint, and low-illumination conditions. Moreover, the traditional single-view palmprint representation methods have been difficult to express the characteristics of each palm strongly, where the palmprint characteristics become weak. To tackle these issues, in this article, we propose a structure suture learning-based robust multiview palmprint recognition method (SSL_RMPR), which comprehensively presents the salient palmprint features from multiple views. Unlike the existing multiview palmprint representation methods, SSL_RMPR introduces a structure suture learning strategy to produce an elastic nearest neighbor graph (ENNG) on the reconstruction errors that simultaneously exploit the label information and the latent consensus structure of the multiview data, such that the discriminant palmprint representation can be adaptively enhanced. Meanwhile, a low-rank reconstruction term integrating with the projection matrix learning is proposed, in such a manner that the robustness of the projection matrix can be improved. Particularly, since no extra structure capture term is imposed into the proposed model, the complexity of the model can be greatly reduced. Experimental results have proven the superiority of the proposed SSL_RMPR by achieving the best recognition performances on a number of real-world palmprint databases.

A Self-Attention CycleGAN for Cross-Domain Semi-Supervised Contactless Palmprint Recognition

Nowadays, there is a growing concern about contactless palmprint recognition because of its high-recognition rate, efficiency, and convenience. With the development of image acquisition equipment, it is an often case that the palmprint images for identification and for registration are captured by different devices. At the same time, a large amount of well-labeled palmprint images are difficult to collect. Therefore, the performance of most existing contactless palmprint recognition methods will be poor in real-life applications. To address these issues, we proposed a self-attention CycleGAN for cross-domain semi-supervised palmprint recognition. Based on CycleGAN, the styles of contactless palmprint images in source domain and target domain can be swapped. Specifically, the spatial features are captured through self-attention modules by modeling long-range dependencies. In addition, an extra source domain classifier is trained with the labeled source domain images to give the unlabeled images in target domain a pseudo-label, by which images in target domain are efficiently utilized. The experiment results showed that our method achieved competitive performance.

A Realistic Hand Image Composition Method for Palmprint ROI Embedding Attack

Palmprint recognition (PPR) has recently garnered attention due to its robustness and accuracy. Many PPR methods rely on preprocessing the region of interest (ROI). However, the emergence of ROI attacks capable of generating synthetic ROI images poses a significant threat to PPR systems. Despite this, ROI attacks are less practical since PPR systems typically take hand images as input rather than just the ROI. Therefore, there is a pressing need for a method that specifically targets the system by composing hand images. The intuitive approach involves embedding an ROI into a hand image, a comparatively simpler process requiring less data than generating entirely synthetic images. However, embedding faces challenges, as the composited hand image must maintain a consistent color and texture. To overcome these challenges, we propose a training-free, end-to-end hand image composition method incorporating ROI harmonization and palm blending. The ROI harmonization process iteratively adjusts the ROI to seamlessly integrate with the hand using a modified style transfer method. Simultaneously, palm blending employs a pretrained inpainting model to composite a hand image with a continuous transition. Our results demonstrate that the proposed method achieves a high attack performance on the IITD and Tongji datasets, with the composited hand images exhibiting realistic visual quality.

Deep and Shallow Feature Fusion in Feature Score Level for Palmprint Recognition

Contactless palmprint recognition offers friendly customer experience due to its ability to operate without touching the recognition device under rigid constrained conditions. Recent palmprint recognition methods have shown promising accuracy; however, there still exist some issues that need to be further studied such as the limited discrimination of the single feature and how to effectively fuse deep features and shallow features. In this paper, deep features and shallow features are integrated into a unified framework using feature‐level and score‐level fusion methods. Specifically, deep feature is extracted by residual neural network (ResNet), and shallow features are extracted by principal component analysis (PCA), linear discriminant analysis (LDA), and competitive coding (CompCode). In feature‐level fusion stage, ResNet feature and PCA feature are dimensionally reduced and fused by canonical correlation analysis technique to achieve the fused feature for the next stage. In score‐level fusion stage, score information is embedded in the fused feature, LDA feature, and CompCode feature to obtain a more reliable and robust recognition performance. The proposed method achieves competitive performance on Tongji dataset and demonstrates more satisfying generalization capabilities on IITD and CASIA datasets. Comprehensive validation across three palmprint datasets confirms the effectiveness of our proposed deep and shallow feature fusion approach.

Adversarial Detection and Fusion Method for Multispectral Palmprint Recognition

As a kind of promising biometric technology, multispectral palmprint recognition methods have attracted increasing attention in security due to their high recognition accuracy and ease of use. It is worth noting that although multispectral palmprint data contains rich complementary information, multispectral palmprint recognition methods are still vulnerable to adversarial attacks. Even if only one image of a spectrum is attacked, it can have a catastrophic impact on the recognition results. Therefore, we propose a robustness-enhanced multispectral palmprint recognition method, including a model interpretability-based adversarial detection module and a robust multispectral fusion module. Inspired by the model interpretation technology, we found there is a large difference between clean palmprint and adversarial examples after CAM visualization. Using visualized images to build an adversarial detector can lead to better detection results. Finally, the weights of clean images and adversarial examples in the fusion layer are dynamically adjusted to obtain the correct recognition results. Experiments have shown that our method can make full use of the image features that are not attacked and can effectively improve the robustness of the model.

Learning Spectrum-Invariance Representation for Cross-Spectral Palmprint Recognition

Palmprint recognition provides a potential solution for noninvasive personal authentication due to its excellent contactless property and user-security, and it has attracted tremendous research interest in recent years. However, most existing methods focus on intraspectral palmprint recognition, which requires gallery and probe images to be captured under similar illumination, and thus significantly limit its practical applications in open environments with variant illuminations. In this study, we present a spectrum-invariant feature learning method for cross-spectral palmprint recognition to address the problem that gallery and probe samples are captured under different spectra. First, the blockwise direction-based ordinal measure vectors are formed to represent the intrinsic information of palmprint images. Then, a unified feature projection is jointly learned to map two different spectra of palmprint images into a common feature space, in which the different spectral features have enhanced discriminative power by enlarging their variances while the intraclass features learned from different spectral images are similar. The proposed method can be easily extended to seek the unified spectrum-invariant representation of multiple spectral palmprint images, making it feasible to perform palmprint recognition crossing one spectrum to multiple spectra. Experimental results on two multispectral palmprint image databases demonstrate the promising effectiveness of the proposed method on cross-spectral palmprint recognition.

Palmprint recognition based on gating mechanism and adaptive feature fusion.

As a type of biometric recognition, palmprint recognition uses unique discriminative features on the palm of a person to identify his/her identity. It has attracted much attention because of its advantages of contactlessness, stability, and security. Recently, many palmprint recognition methods based on convolutional neural networks (CNN) have been proposed in academia. Convolutional neural networks are limited by the size of the convolutional kernel and lack the ability to extract global information of palmprints. This paper proposes a framework based on the integration of CNN and Transformer-GLGAnet for palmprint recognition, which can take advantage of CNN's local information extraction and Transformer's global modeling capabilities. A gating mechanism and an adaptive feature fusion module are also designed for palmprint feature extraction. The gating mechanism filters features by a feature selection algorithm and the adaptive feature fusion module fuses them with the features extracted by the backbone network. Through extensive experiments on two datasets, the experimental results show that the recognition accuracy is 98.5% for 12,000 palmprints in the Tongji University dataset and 99.5% for 600 palmprints in the Hong Kong Polytechnic University dataset. This demonstrates that the proposed method outperforms existing methods in the correctness of both palmprint recognition tasks. The source codes will be available on https://github.com/Ywatery/GLnet.git.

Multiview-Learning-Based Generic Palmprint Recognition: A Literature Review

Palmprint recognition has been widely applied to security authentication due to its rich characteristics, i.e., local direction, wrinkle, and texture. However, different types of palmprint images captured from different application scenarios usually contain a variety of dominant features. Specifically, the palmprint recognition performance will be degraded by the interference factors, i.e., noise, rotations, and shadows, while palmprint images are acquired in the open-set environments. Seeking to handle the long-standing interference information in the images, multiview palmprint feature learning has been proposed to enhance the feature expression by exploiting multiple characteristics from diverse views. In this paper, we first introduced six types of palmprint representation methods published from 2004 to 2022, which described the characteristics of palmprints from a single view. Afterward, a number of multiview-learning-based palmprint recognition methods (2004–2022) were listed, which discussed how to achieve better recognition performances by adopting different complementary types of features from multiple views. To date, there is no work to summarize the multiview fusion for different types of palmprint features. In this paper, the aims, frameworks, and related methods of multiview palmprint representation will be summarized in detail.

Multi-Spectral Palmprints Joint Attack and Defense With Adversarial Examples Learning

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">As an emerging biometric technology, multi-spectral palmprint recognition has attracted increasing attention in security due to its high accuracy and ease of use. Compared to single spectral case, multi-spectral palmprint model is more susceptible to the attack of adversarial examples. However, the previous adversarial example attack approaches cannot generate the most aggressive adversarial examples for multi-spectral palmprint recognition. In addition, most of them are dependent on the explicit architecture or need time-consuming queries about the network to be attacked, which significantly limits their application in the field of security. To solve the above problems, in this paper, we proposed the multi-spectral palmprints joint attack and defense framework based on multi-view adversarial examples learning. First, we respectively capture the multi-view deep common feature space for the different spectra and the discriminative feature space across the different subjects. Second, we introduce perturbation in the deep common space to achieve adversarial multi-spectral palmprints with gradient propagation. In addition, we pursue the manifold of the difference space and use it to suppress the discriminability of the recognition model with adversarial region theory. Finally, the generated adversarial examples are fed into the training model to enhance the robustness of the recognition algorithm. The experimental results on multi-spectral palmprint dataset demonstrate that the proposed multi-view joint attack approach is superior to the state-of-the-art adversarial example attack methods in attack accuracy and transferability. Moreover, the defense strategy with the adversarial examples by our method can significantly promote the robustness of multi-spectral palmprint recognition methods</i> .

EEPNet: An efficient and effective convolutional neural network for palmprint recognition

Palmprint recognition is an important biometrics technology. In recent years, a lot of palmprint recognition methods based on convolution neural networks (CNNs) have been proposed. However, existing CNNs specially designed for palmprint recognition have high computational complexity. In order to make palmprint recognition method based on deep learning work well on mobile devices, lightweight neural networks must be used. However, up to now, there is very little research on this topic. In this paper, we propose an efficient and effective palmprint recognition network (EEPNet), which is a lightweight neural network. EEPNet is designed based on MobileNet-V3, and further compresses the number of layers and enlarges the convolution kernel. In addition, we design two new loss functions including Balanced Loss and Contrast Loss. Balanced Loss is suitable for various specific data sets, while Contrast Loss can achieve the purpose of training difficult samples without manual parameter adjustment. According to the characteristics of palmprint recognition, we add five strategies to improve the recognition performance including image splicing, image dimension reduction, data augmentation, cascade channel attention mechanism, and hard case mining mechanism. We conduct thorough experiments on seven palmprint databases. The experimental results show that the overall recognition performance of our method outperforms classic and state-of-the-art palmprint recognition methods on the palmprint databases with normal quality. We compare our method with other CNNs in four aspects: precision, speed, parameter quantity and FLOPs. The experimental results show that our method is more efficient and has high recognition accuracy.

Double-cohesion learning based multiview and discriminant palmprint recognition

Palmprint recognition has been widely used in security authentication. However, most of the existing palmprint representation methods are focused on a special application scenario using the hand-crafted features from a single-view. If the features become weak as the application scenario changes, the recognition performance will be degraded. To address this problem, we propose to comprehensively exploit palmprint features from multiple views to improve the recognition performance in generic scenarios. In this paper, a novel double-cohesion learning based multiview and discriminant palmprint recognition (DC_MDPR) method is proposed, which imposes a double-cohesion strategy to reduce the inter-view margins for each subject and the intra-class margins for each view. In this way, for each subject, the features from different views can be closer to each other in the binary-label space. Meanwhile, for each view, the features sharing the same label information can move towards each other by imposing a neighbor graph regularization. The proposed method can be flexibly applied to any type of palmprint feature fusion. Moreover, it presents the multiview features in a low-dimensionality sub-space, effectively reducing the computational complexity. Experimental results on various palmprint databases have shown that the proposed method can always achieve the best recognition performance compared to other state-of-the-art algorithms.

Towards open-set touchless palmprint recognition via weight-based meta metric learning

Touchless biometrics has become significant in the wake of novel coronavirus 2019 (COVID-19). Due to the convenience, user-friendly, and high-accuracy, touchless palmprint recognition shows great potential when the hygiene issues are considered during COVID-19. However, previous palmprint recognition methods are mainly focused on close-set scenario. In this paper, a novel Weight-based Meta Metric Learning (W2ML) method is proposed for accurate open-set touchless palmprint recognition, where only a part of categories is seen during training. Deep metric learning-based feature extractor is learned in a meta way to improve the generalization ability. Multiple sets are sampled randomly to define support and query sets, which are further combined into meta sets to constrain the set-based distances. Particularly, hard sample mining and weighting are adopted to select informative meta sets to improve the efficiency. Finally, embeddings with obvious inter-class and intra-class differences are obtained as features for palmprint identification and verification. Experiments are conducted on four palmprint benchmarks including fourteen constrained and unconstrained palmprint datasets. The results show that our W2ML method is more robust and efficient in dealing with open-set palmprint recognition issue as compared to the state-of-the-arts, where the accuracy is increased by up to 9.11% and the Equal Error Rate (EER) is decreased by up to 2.97%.

Triple-Type Feature Extraction for Palmprint Recognition.

Palmprint recognition has received tremendous research interests due to its outstanding user-friendliness such as non-invasive and good hygiene properties. Most recent palmprint recognition studies such as deep-learning methods usually learn discriminative features from palmprint images, which usually require a large number of labeled samples to achieve a reasonable good recognition performance. However, palmprint images are usually limited because it is relative difficult to collect enough palmprint samples, making most existing deep-learning-based methods ineffective. In this paper, we propose a heuristic palmprint recognition method by extracting triple types of palmprint features without requiring any training samples. We first extract the most important inherent features of a palmprint, including the texture, gradient and direction features, and encode them into triple-type feature codes. Then, we use the block-wise histograms of the triple-type feature codes to form the triple feature descriptors for palmprint representation. Finally, we employ a weighted matching-score level fusion to calculate the similarity between two compared palmprint images of triple-type feature descriptors for palmprint recognition. Extensive experimental results on the three widely used palmprint databases clearly show the promising effectiveness of the proposed method.

Improvement Palmprint Recognition System by Adjusting Image Data Reference Points

Nowadays, the palmprint recognition method is an attractive tool for people recognition besides face recognition and fingerprint. To get a good palmprint recognition system requires a series of algorithms that are united and worked together to create a preferred method. The biometric system in palmprint recognition is said to be preferred when the choice of the algorithm applied has the advantage of each phase. Generally, The palmprint recognition system starts from the selection of image filters, image positioning, dimension reduction, and finally, end with the distance method. Image normalization position is a problem for researchers because the palmprint is always moving and shows a curved shape making it difficult for the system in the identification and verification process. For this reason, the research focuses on the selection of orientation scale operator value from the Gabor technique to get the best parameter value for palmprint biometrics. The desired system is when the overall performance of the algorithm used will produce an output with a low EER value and a high level of verification. From the research that has been carried out, the range of 8 × 5 becomes a good alternative for the choice of Gabor value pairs.

Jointly Heterogeneous Palmprint Discriminant Feature Learning.

Heterogeneous palmprint recognition has attracted considerable research attention in recent years because it has the potential to greatly improve the recognition performance for personal authentication. In this article, we propose a simultaneous heterogeneous palmprint feature learning and encoding method for heterogeneous palmprint recognition. Unlike existing hand-crafted palmprint descriptors that usually extract features from raw pixels and require strong prior knowledge to design them, the proposed method automatically learns the discriminant binary codes from the informative direction convolution difference vectors of palmprint images. Differing from most heterogeneous palmprint descriptors that individually extract palmprint features from each modality, our method jointly learns the discriminant features from heterogeneous palmprint images so that the specific discriminant properties of different modalities can be better exploited. Furthermore, we present a general heterogeneous palmprint discriminative feature learning model to make the proposed method suitable for multiple heterogeneous palmprint recognition. Experimental results on the widely used PolyU multispectral palmprint database clearly demonstrate the effectiveness of the proposed method.

Multispectral Palm Print and Palm Vein Acquisition Platform and Recognition Method Based on Convolutional Neural Network

Abstract Biometrics recognition takes advantage of feature extraction and pattern recognition to analyze the physical and behavioral characteristics of biological individuals to achieve the purpose of individual identification. As a typical biometric technology, palm print and palm vein have the characteristics of high recognition rate, stable features, easy location and good image quality, which have attracted the attention of researchers. This paper designs and develops a multispectral palm print and palm vein acquisition platform, which can quickly acquire palm spectrum and palm vein multispectral images with seven different wavelengths. We propose a multispectral palm print palmar vein recognition framework, and feature-level image fusion is performed after extracting features of palm print palmar vein images at different wavelengths. Through the multispectral palm print palm vein image fusion experiment, a more feasible multispectral palm print and palm vein image fusion scheme is proposed. Based on the results of image fusion, we further propose an improved convolutional neural network (CNN) for model training to achieve identity recognition based on multispectral palm print palm vein images. Finally, the effects of different CNN network structures and learning rates on the recognition results were analyzed and compared experimentally.

One-shot cross-dataset palmprint recognition via adversarial domain adaptation

Deep learning-based palmprint recognition algorithms have obtained promising performance. However, the previous methods require a large amount of labeled samples, which are difficult to obtain. In this paper, a novel cross-dataset palmprint recognition method is proposed using as low as one labelled sample per subject in the target palmprint dataset based on adversarial domain adaptation. Two different palmprint datasets are adopted as source dataset and target dataset. The training samples from two datasets are grouped into four categories. MobileFaceNets-based deep hashing network (DHN) is introduced to extract discriminative features, which can improve the efficiencies of feature extracting and matching. To align the features in two datasets, a typical adversarial discriminator is augmented to distinguish between the four different categories. With adversarial learning, the target network is becoming adaptive to the unlabeled target palmprint images. Extensive experiments on the benchmarks including constrained and unconstrained palmprint databases demonstrate that our method can outperform the baseline models on cross-dataset palmprint verification and identification.

Learning Complete and Discriminative Direction Pattern for Robust Palmprint Recognition

Palmprint direction patterns have been widely and successfully used in palmprint recognition methods. Most existing direction-based methods utilize the pre-defined filters to achieve the genuine line responses in the palmprint image, which requires rich prior knowledge and usually ignores the vital direction information. In addition, some line responses influenced by noise will degrade the recognition accuracy. Furthermore, how to extract the discriminative features to make the palmprint more separable is also a dilemma for improving the recognition performance. To solve these problems, we propose to learn complete and discriminative direction patterns in this study. We first extract the complete and salient local direction patterns, which contains a complete local direction feature (CLDF) and a salient convolution difference feature (SCDF) extracted from the palmprint image. Afterwards, two learning models are proposed to learn sparse and discriminative directions from CLDF and to achieve the underlying structure for the SCDFs in the training samples, respectively. Lastly, the projected CLDF and the projected SCDF are concatenated forming the complete and discriminative direction feature for palmprint recognition. Experimental results on seven palmprint databases, as well as three noisy datasets clearly demonstrates the effectiveness of the proposed method.

Towards privacy palmprint recognition via federated hash learning

Nowadays, deep learning-based palmprint recognition methods have achieved great success. However, they are mainly focused on the accuracy and ignore the privacy, which is more important in the practical applications. In this Letter, a novel method, federated hash learning (FHL), is proposed for privacy palmprint recognition. There are several agents deploy in different communities, and they have different models and private data. An available public dataset is introduced to provide communications for each agent. Through appropriate federated loss, the agents are connected to help each other train the models to improve the accuracy. Experiments are conducted on constrained and unconstrained palmprint benchmarks. The results demonstrate that the authors’ FHL can outperform other baselines and obtain promising accuracy.

Extreme Downsampling and Joint Feature for Coding-Based Palmprint Recognition

Palmprint is a commonly used and significant modality for biometric recognition currently. The coding-based methods are practical for palmprint recognition because they can be free from training, and have low computational complexity and storage cost. Downsampling is widely used in encoding-based methods for storage cost reduction and matching speed acceleration. In the traditional downsampling method (TDM), the feature of each block is only determined by its upper left pixel; however, TDM totally ignores the other useful pixels. We propose a practical but effective general downsampling method, dubbed extreme downsampling method (EDM). In EDM, the best response pixel in each block is selected as the representative. Because the winner is selected from the 16 candidates in each block, EDM substantially improves the robustness against the dislocation due to imperfect preprocessing, and accordingly improves the accuracy performance. In addition, we propose a joint feature method (JFM) to fuse the matching distances of the templates at score level, which are downsampled by TDM and EDM. The accuracy performance and robustness of EDM and JFM are solidly confirmed when they are employed on several state-of-the-art coding-based palmprint recognition methods.