Palmprint Images Research Articles

Toward Large-Scale Palmprint Image Analysis by a Rich Orientation Code

Toward Large-Scale Palmprint Image Analysis by a Rich Orientation Code

Gunet: a novel and efficient low-illumination palmprint image enhancement method

Gunet: a novel and efficient low-illumination palmprint image enhancement method

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.

Palmprint spoofing detection by using deep learning technique on Multispectral database

The protection of Biometric systems against attacks is crucial as biometric devices proliferate in the field of personal authentication. The presentation assault is the most prevalent type of attack on biometric systems; it entails presenting a fake copy (artefact) of the true biometric to the sensor in order to gain unauthorised access. The vulnerability in palmprint-based biometric systems has not received much attention despite the substantial threat posed by these assaults. In this research, we show how to detect a spoof palmprint image. Spoofing attacks involving faked images pose a significant threat to biometric systems. For the suggested method, we use the CASIA palmprint database, from which we constructed our own spoof database using printed photos. After that, we did some pre-processing to obtain the ROI image and a noise-free image for feature extraction using the SIFT approach. We use the convolution neural network for classification and the SVM for comparison. We obtained a result of 96.2% for our proposed palmprint system identification and 89% for SVM. But our main goal is to train the model for spoof detection, so we take some normal images and some spoof images for our train model and use the confusion matrix to calculate the accuracy of our model. We obtain an overall accuracy of 86% for our spoof detection by computing the confusion matrix.

LDBM-PFR: Local Directional Binary Pattern Mixed Filtering for 2D and 3D Palmprint Fusion Recognition

<p>Single-dimensional palmprint images are susceptible to interference from noise, lighting, and various factors, leading to information loss and subpar recognition performance. To address the problem, a fusion recognition scheme of 2D and 3D palmprints based on Hybrid Filter Local Orientation Binary Pattern (HFLOBP) is proposed. Firstly, the HFLOBP algorithm is employed to extract 2D features and 3D Shape Index (SI) features respectively to improve the recognition effect. Secondly, Principal Component Analysis (PCA) is utilized to improve the recognition efficiency, to reduce the dimension of 2D features and 3D Surface Type (ST) features of palmprint, to improve the recognition efficiency. Finally, the multi-dimensional palmprint features are fused by Double Weight Threshold Algorithm (DWTA), and classified and recognized by Collaborative Representation (CR). Experimental results conducted on the 3D palmprint database of Hong Kong Polytechnic University reveal that, in comparison with alternative classification methods, the average recognition rate reaches 99.98%, with an average recognition time of 0.64 seconds. The proposed method not only demonstrates a commendable recognition effect but also meets the real-time requirements, underscoring its practical application value.</p> <p> </p>

SYEnet: Simple yet effective network for palmprint recognition

Palmprint recognition techniques have been widely applied in security authentication. As a typical image processing method, convolutional neural network (CNN) has been applied to extract features from palmprint images. However, most existing CNN methods cannot meet the demand in recognition speed due to their high computational complexity. This paper proposes a simple yet effective network (SYEnet) for palmprint recognition, which is a lightweight neural network. SYEnet is composed of four learnable feature extraction networks, which can extract discriminative information from 9 patches. In addition, a dedicated network for palmprint ROI extraction (PREnet) is designed. PREnet extracts ROI from the original image, generating ROI images of consistent dimensions, which can be adapted to complex environments without limitations of lighting, gesture, image quality, and resolution. Moreover, a new contactless palmprint database is collected to validate our method. The database contains 12,800 images from 400 palms and provides data supplementation for current research on palmprint recognition. The proposed SYEnet is evaluated on eight real-world palmprint databases, in comparison to other related methods. Experimental results prove that our method is more efficient and has higher recognition accuracy.

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.

Complete Region of Interest for Unconstrained Palmprint Recognition.

Unconstrained palmprint images have shown great potential for recognition applications due to their lower restrictions regarding hand poses and backgrounds during contactless image acquisition. However, they face two challenges: 1) unclear palm contours and finger-valley points of unconstrained palmprint images make it difficult to locate landmarks to crop the palmprint region of interest (ROI); and 2) large intra-class diversities of unconstrained palmprint images hinder the learning of intra-class-invariant palmprint features. In this paper, we propose to directly extract the complete palmprint region as the ROI (CROI) using the detection-style CenterNet without requiring the detection of any landmarks, and large intra-class diversities may occur. To address this, we further propose a palmprint feature alignment and learning hybrid network (PalmALNet) for unconstrained palmprint recognition. Specifically, we first exploit and align the multi-scale shallow representation of unconstrained palmprint images via deformable convolution and alignment-aware supervision, such that the pixel gaps of the intra-class palmprint CROIs can be minimized in shallow feature space. Then, we develop multiple triple-attention learning modules by integrating spatial, channel, and self-attention operations into convolution to adaptively learn and highlight the latent identity-invariant palmprint information, enhancing the overall discriminative power of the palmprint features. Extensive experimental results on four challenging palmprint databases demonstrate the promising effectiveness of both the proposed PalmALNet and CROI for unconstrained palmprint recognition.

Toward Efficient Palmprint Feature Extraction by Learning a Single-Layer Convolution Network.

In this article, we propose a collaborative palmprint-specific binary feature learning method and a compact network consisting of a single convolution layer for efficient palmprint feature extraction. Unlike most existing palmprint feature learning methods, such as deep-learning, which usually ignore the inherent characteristics of palmprints and learn features from raw pixels of a massive number of labeled samples, palmprint-specific information, such as the direction and edge of patterns, is characterized by forming two kinds of ordinal measure vectors (OMVs). Then, collaborative binary feature codes are jointly learned by projecting double OMVs into complementary feature spaces in an unsupervised manner. Furthermore, the elements of feature projection functions are integrated into OMV extraction filters to obtain a collection of cascaded convolution templates that form a single-layer convolution network (SLCN) to efficiently obtain the binary feature codes of a new palmprint image within a single-stage convolution operation. Particularly, our proposed method can easily be extended to a general version that can efficiently perform feature extraction with more than two types of OMVs. Experimental results on five benchmark databases show that our proposed method achieves very promising feature extraction efficiency for palmprint recognition.

Human identification using palm print images based on deep learning methods and gray wolf optimization algorithm

Human identification using palm print images based on deep learning methods and gray wolf optimization algorithm

Sequence-wise multimodal biometric fingerprint and finger-vein recognition network (STMFPFV-Net)

The existing multimodal biometric fingerprint and vein deep learning features were found effective for biometric recognition. However, the current performance of the deep learning features is limited due to missing temporal image dependence and the extracted features can obfuscate some vital information because of irrelevant image features. This work proposes a sequence of filtered spatial and temporal multimodal fingerprint and finger vein network (FS-STMFPFV-Net). The overall proposed FS-STMFPFV-Net is achieved from two-channel independent learning to improve image variabilities. In the first channel, the image sequence is generated by aligning the fingerprint and finger vein images together inside the image generator. The sequences are built into the five layers of a deep convolution neural network fusion model to extract spatial sequence-wise features. The second channel is where the extracted sequence-wise features are remembered in the long short-term memory for interactions between temporal and sequence dimensions which finally generate their long-term temporal dependencies as complementary information. These sequences are fused together, and the discriminative sequence features are selected using feature selection. We have presented the ReliefFS feature selection which serve as a complementary basis for selecting compact features from the extracted CNN-based features. To evaluate the performance of the proposed FS-STMFPFV-Net, the NUPT-FPV, FVC-2002-DBs, and the CASIA dataset were used, which provides fingerprint, finger-vein, and palmprint image databases, for experimental validation. The proposed FS-STMFPFV-Net when evaluated using standard protocols offers more than 97% accuracy across the different databases and is ten times more computationally friendly than the existing algorithms.

Palm Print Database Collection System

Contactless biometric palm print recognition technology has attracted increased attention due to the covid-19 pandemic. Many dual camera based sensors have been proposed to capture palm vein and palm print image synchronously. However, translations between captured palm print images differ depending on the distance between the hand and the sensor. A biometric palm image Alignment method is proposed based on the imaging and ranging model. A palm print refers to an image acquired of the palm region of the hand. Palm print image enhancement is required for better feature extraction. To extract fine features enhancement is required. Fine features increase the accuracy of palm print recognition system. The main purpose of this review paper is to study and compare various palm print recognition techniques in terms of performance, accuracy, overheads and Peak Signal to Noise Ratio (PSNR)

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.

Learning modality-invariant binary descriptor for crossing palmprint to palm-vein recognition

Palmprint has shown promising potential for biometrics recognition due to its excellent contactless and hygienic properties. However, most existing methods focus only on feature learning of unimodal palmprint images leading to the challenge of palmprint recognition crossing different modalities. In this paper, we propose a modality-invariant binary features learning (MIBFL) method for crossing palmprint to palm-vein recognition, where palm images are captured under visible and invisible near-infrared illumination, respectively. We first map the multiple modal palm images into their high-dimensional alignment representation to reduce the impact of misalignment and noise between different image modalities. Then, we simultaneously learn the discriminative features from different modalities of alignment images via matrix factorization by enforcing the orthogonal and balanced constraints. Lastly, we jointly learn a pair of encoding functions to project multi-modal palm features into the common binary feature descriptor for crossing palmprint to palm-vein recognition. Experimental results on the widely used PolyU multi-spectral palmprint database are presented to demonstrate the effectiveness of the proposed MIBFL method.

Palmprint authentication using symbolic aggregate approximation features with improved enhancement techniques

The palmprint enhancement is a pre-processing stage of palmprint authentication. The accuracy of the recognition rate can be improved by incorporating effective palmprint enhancement methods. So far, only a little work has been done in palmprint enhancement. Little attention is given to incorporate the enhancement techniques in palmprint authentication system to achieve performance improvement. This work presents robust enhancement methods which provide better performance and accuracy. Palmprint enhancement using curvelet and Recursive Histogram Equalization (RHE) overcomes the drawbacks of the existing systems and makes palmprint recognition simpler and more accurate with enhanced palm image as input. Palmprint recognition system uses the Symbolic Aggregate Approximation (SAX) features from the enhanced palmprint image. The recognition rate is optimum when both curvelet and RHE methods are used for enhancement of palmprint. Here we compared different types of enhancement methods and we have obtained maximum recognition rate with a combination of recursive histogram equalization and curvelet transform. The performance of the palmprint authentication is measured by False Acceptance Rate (FAR), False Rejection Rate (FRR) and Total Success Rate (TSR). The comparison of recognition rate with enhancement is done using minimum distance classifier, Support Vector Machine, Random Forest and Bayesian. Random Forest classifier provides better results.

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.

IMPLEMENTATION OF ACCURATE PERSONAL IDENTIFICATION BY USING PALM PRINT IMAGE PROCESSING

Online palmprint recognition and latent palmprint identification are two branches of palmprint studies. The former uses middle-resolution images collected by a digital camera in a well-controlled or contact-based environment with user cooperation for commercial applications and the latter uses highresolution latent palmprints collected in crime scenes for forensic investigation. However, these two branches do not cover some palmprint images which have the potential for forensic investigation. Due to the prevalence of smartphone and consumer camera, more evidence is in the form of digital images taken in uncontrolled and uncooperative environment, e.g., child pornographic images and terrorist images, where the criminals commonly hide or cover their face. However, their palms can be observable. Among various biometrics technologies, palm-print identification has received much attention because of its good performance. Combining the left and right palm-print images to perform multi-biometrics is easy to implement and can obtain better results. Existing systems deployed Line Based Method, Coding Based Method, Subspace Based Methods, Representation Based Method, SIFT Based Method. This work integrated three kinds of scores generated from the left and right palm-print images to perform matching score-level fusion. The first two kinds of scores were, respectively, generated from the left and right palm-print images and can be obtained by any palm-print identification method, whereas the third kind of score was obtained using a specialized algorithm proposed in this paper. As the proposed algorithm carefully takes the nature of the left and right palm-print images into account, it can properly exploit the similarity of the left and right palm-prints of the same subject. Moreover, the proposed weighted fusion scheme allowed perfect identification performance to be obtained in comparison with previous palm-print identification methods.

BEST: Building evidences from scattered templates for accurate contactless palmprint recognition

Contactless palmprint identification offers significantly improved hygiene and user convenience, making it highly attractive for a range of civilian applications, especially during the current pandemic. However, the accurate recognition of contactless palmprint images can be highly challenging, attributed to the significant variations in the intra-class similarity and limitations of conventional palmprint feature descriptors under involuntary or contactless imaging variations. State-of-the-art completely contactless palmprint matching algorithms in the literature cannot adequately address these challenges and are not sufficiently accurate and fast enough for such real-world applications. This paper proposes a novel approach that adaptively locates the local palmprint regions with high similarities between their corresponding feature representations or templates to address these challenges. We consider spatial localization of such highly similar feature representations from multiple local regions and consolidate them to generate a more reliable match score. This paper presents reproducible and comparative experimental results, using within-database, cross-database, and cross-sensor performance evaluation, on four publicly available contactless palmprint datasets, including a sizeable contactless palmprint database from 600 different subjects. The proposed method achieves outperforming results compared with three state-of-the-art deep learning-based methods and five widely used conventional methods. In addition, the proposed method is also significantly faster than all state-of-the-art baseline methods.

Authentication using hand gesture

In some real-world personal identification applications requiring high security standards, multibiometrics can provide higher identification accuracy than single biometrics. Due to its good performance, palmprint identification has received a lot of attention among other biometrics technologies. Combining left and right palmprints for multibiometrics is easy to implement and can produce better results. However, previous studies did not explore this issue in depth. An innovative framework for performing multibiometrics by combining left and right palmprint images is presented in this paper. This framework integrated three kinds of scores generated from the left and right palmprint images to perform matching score-level fusion. These first two scores were generated from left and right palmprint images, respectively, and can be obtained using any palmprint identification method. However, the third score was obtained using a specialized algorithm. Due to the careful consideration of the left and right palmprint images, the proposed algorithm can effectively exploit the similarity between the left and right palmprints. Moreover, the proposed weighted fusion scheme allowed perfect identification performance to be obtained in comparison with previous palmprint identification methods.

PALM IDENTIFICATION

Multibiometrics can provide higher identification accuracy than single biometrics, so it is more suitable for some real-world personal identification applications that need high-standard security. Among various biometrics technologies, palmprint identification has received much attention because of its good performance. Combining the left and right palmprint images to perform multibiometrics is easy to implement and can obtain better results. However, previous studies did not explore this issue in depth. In this paper, we proposed a novel framework to perform multibiometrics by comprehensively combining the left and right palmprint images. This framework integrated three kinds of scores generated from the left and right palmprint images to perform matching score-level fusion. The first two kinds of scores were, respectively, generated from the left and right palmprint images and can be obtained by any palmprint identification method, whereas the third kind of score was obtained using a specialized algorithm proposed in this paper. As the proposed algorithm carefully takes the nature of the left and right palmprint images into account, it can properly exploit the similarity of the left and right palmprints of the same subject. Moreover, the proposed weighted fusion scheme allowed perfect identification performance to be obtained in comparison with previous palmprint identification methods.