Palmprint Recognition Systems Research Articles

Image enhancement in palmprint recognition: a novel approach for improved biometric authentication

Several researchers have used image enhancement methods to reduce detection errors and increase verification accuracy in palmprint identification. Divergent opinions exist among experts regarding the best method of image filtering to improve image palmprint recognition. Because of the unique characteristics of palmprints and the difficulties in preventing counterfeiting, image-filtering techniques are the subject of this current research. Researchers hope to create the best biometric system possible by utilizing various techniques. These techniques include image enhancement, Gabor orientation scales, dimension reduction techniques, and appropriate matching strategies. This study investigates how different filtering approaches might be combined to improve images. The palmprint identification system uses a 3W filter, which combines wavelet, Wiener, and weighted filters. Optimizing results entails coordinating the 3W filter with Gabor orientation scales, matching processes, and dimension reduction methods. The research shows that accuracy may be considerably increased using a 3W filter with a Gabor orientation scale of [8×7], the kernel principal component analysis (KPCA) dimension reduction methodology, and a cosine matching method. Specifically, a value of 99.722% can be achieved. These results highlight the importance of selecting appropriate settings and techniques for palmprint recognition systems.

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.

A Secure and Efficient Biometric Template Protection Scheme for Palmprint Recognition System

The accuracy and security of a biometric system are the two sides of a coin. A biometric system must be simple, flexible, efficient, and secure enough from unauthorized access. Concerning these requirements, this paper presents an enhanced palmprint recognition system with superior performance and improved biometric template protection schemes. This method comprises five components: image preprocessing, feature extraction, cancelable biometrics, classification, and Bio-cryptosystem. The palm texture region is extracted from the input hand image during preprocessing. A feature representation technique is then used to extract discriminant features from the palmprint texture patterns. The extracted features are then analyzed using an information coding scheme to compute a user-specific token. Here, the user-specific tokens along with the original palmprint features are used to calculate the feature vectors for the cancelable biometric system. A novel Bio-Cryptosystem has been proposed to protect templates with high security where there is no need to remember the users&#x2019; tokens. Cancelable feature vectors are kept online in encrypted form using the proposed Bio-Cryptosystem, which is used to identify or verify the subjects after decryption. The original feature vectors are kept offline to prevent these from external attacks and misuse. Finally, the multi-class linear support vector machine has been used for user classification. For experimental purposes, two benchmark hand image databases: BOSPHORUS and CASIA-Palmprint, are employed, and the proposed system has achieved nearly 100&#x0025; correct recognition rate in cancelable domain for both the employed databases. Finally, the performance comparison in terms of both verification and identification for the palmprint and the cancelable palmprint recognition systems with the existing state-of-the-art methods shows the superiority of the proposed system. <i>Impact Statement</i> &#x2014;We have improved the biometric template protection scheme by modifying the cancelable biometric technique with two security levels capable of protecting the biometric information from the dictionary, brute-force, database, channel, replay, and spoofing attacks. To avoid the difficulty of remembering long user passwords/tokens, an automatic key generation technique of user passwords has been proposed. Two different keys are generated: the first one used for cancelable biometrics and the second for encryption-decryption processes. Both these keys are not needed to store or remember and are helpful for a multi-factor authentication system. A novel Bio-cryptographic algorithm instead of traditional Bio-Cryptographic algorithms have been introduced within the proposed system. Several attacks and challenging issues of biometric recognition systems have been considered with maintaining outstanding performance in terms of accuracy and security.

Cross-database attack of different coding-based palmprint templates

A cross-database attack in biometric systems is a security attack where attackers attempt to leverage a compromised target user’s template in one database and infer their templates in other databases. The biometric community largely ignores cross-database attack although they pose poses potential severe risks to the security of the biometric systems. This paper presents a comprehensive study on cross-database attacks in palmprint recognition systems. We specifically focus on the coding-based palmprint templates due to their popularity. Coding-based methods for palmprint feature representation are designed differently to improve performance accuracy and reduce complexity, where the coded templates look completely diverse; thus, it is difficult to correlate them in a meaningful way. However, we demonstrate that the latent correlation of coding-based palmprint templates can indeed be established. Specifically, we analyze six coding-based palmprint representations, and by exploiting the latent statistical correlations among them, we devise an effective cross-database attack algorithm. The attack enables the target users’ palmprint templates to be exploited to infer their templates stored in other databases despite different coding methods. Our cross-database attack even yields a 100% success rate in some scenarios on the public datasets. This suggests high risks of cross-database attacks and privacy invasion of palmprint recognition systems that adopt coding-based representation.

A Single Objective GA and PSO for the Multimodal Palmprint Recognition System

Biometric plays a vital role in human authentication systems. Unimodal and multimodal biometrics have been active research areas for the past few decades. The investigation of palmprint recognition under various illuminations, rotations, and translations is a challenging task. The research work on multimodal palmprint recognition systems has widely increased to improve the recognition rate and reduce execution time. In this article, a multimodal palmprint biometric system is formed by combining the left and right palmprint images to obtain an optimal recognition rate. A modified multilobe ordinal filter (MMLOF) is used to extract the features. Feature-level fusion is used to fuse the left and right palmprint images. This results in a high-dimension feature vector that requires larger memory to store. It creates redundant and irrelevant features that affect the recognition rate. To overcome these limitations, the optimal MMOF features are extracted by optimization techniques such as particle swarm optimization (PSO) and the genetic algorithm (GA). Finally, PSO and GA optimization algorithms are wrapped with the nearest neighbor classifier (NN) to evaluate the fitness function. The experimental analyses are conducted to identify the performance of GA and PSO using the IITD palmprint dataset. The 1st order MMLOF with GA (multimodal) converges faster and outperforms the 1st order MMLOF with PSO (multimodal) and obtains an optimal recognition rate of 96.95%.

Deep learning approach for Touchless Palmprint Recognition based on Alexnet and Fuzzy Support Vector Machine

Due to stable and discriminative features, palmprint-based biometrics has been gaining popularity in recent years. Most of the traditional palmprint recognition systems are designed with a group of hand-crafted features that ignores some additional features. For tackling the problem described above, a Convolution Neural Network (CNN) model inspired by Alex-net that learns the features from the ROI images and classifies using a fuzzy support vector machine is proposed. The output of the CNN is fed as input to the fuzzy Support vector machine. The CNN's receptive field aids in extracting the most discriminative features from the palmprint images, and Fuzzy SVM results in a robust classification. The experiments are conducted on popular contactless datasets such as IITD, POLYU2, Tongji, and CASIA databases. Results demonstrate our approach outperformers several state-of-art techniques for palmprint recognition. Using this approach, we obtain 99.98% testing accuracy for the Tongji dataset and 99.76 % for the POLYU-II datasets.

Presentation Attacks in Palmprint Recognition Systems

Background: A presentation attack places the printed image or displayed video at the front of the sensor to deceive the biometric recognition system. Usually, presentation attackers steal a genuine user’s biometric image and use it for presentation attack. In recent years, reconstruction attack and adversarial attack can generate high-quality fake images, and have high attack success rates. However, their attack rates degrade remarkably after image shooting. Methods: In order to comprehensively analyze the threat of presentation attack to palmprint recognition system, this paper makes six palmprint presentation attack datasets. The datasets were tested on texture coding-based recognition methods and deep learning-based recognition methods. Results and conclusion: The experimental results show that the presentation attack caused by the leakage of the original image has a high success rate and a great threat; while the success rates of reconstruction attack and adversarial attack decrease significantly.

Learning to Combine Local and Global Image Information for Contactless Palmprint Recognition.

In the past few years, there has been a leap from traditional palmprint recognition methodologies, which use handcrafted features, to deep-learning approaches that are able to automatically learn feature representations from the input data. However, the information that is extracted from such deep-learning models typically corresponds to the global image appearance, where only the most discriminative cues from the input image are considered. This characteristic is especially problematic when data is acquired in unconstrained settings, as in the case of contactless palmprint recognition systems, where visual artifacts caused by elastic deformations of the palmar surface are typically present in spatially local parts of the captured images. In this study we address the problem of elastic deformations by introducing a new approach to contactless palmprint recognition based on a novel CNN model, designed as a two-path architecture, where one path processes the input in a holistic manner, while the second path extracts local information from smaller image patches sampled from the input image. As elastic deformations can be assumed to most significantly affect the global appearance, while having a lesser impact on spatially local image areas, the local processing path addresses the issues related to elastic deformations thereby supplementing the information from the global processing path. The model is trained with a learning objective that combines the Additive Angular Margin (ArcFace) Loss and the well-known center loss. By using the proposed model design, the discriminative power of the learned image representation is significantly enhanced compared to standard holistic models, which, as we show in the experimental section, leads to state-of-the-art performance for contactless palmprint recognition. Our approach is tested on two publicly available contactless palmprint datasets—namely, IITD and CASIA—and is demonstrated to perform favorably against state-of-the-art methods from the literature. The source code for the proposed model is made publicly available.

A Calibration Approach to Solve Inconsistency Problem in Palmprint Acquisition System

Inconsistency among different acquisition devices severely affects the performance of palmprint recognition systems. When the same palm is enrolled by one capture device and verified by another device, the inconsistency between the two devices may lead to recognition failure. The installation of camera and the setting of lighting are two main reasons for inconsistency problem in palmprint recognition systems. In this article, we proposed a calibration approach for the inconsistency problem regarding palmprint acquisition devices. Our calibration approach includes image quality assessment, rectification and normalization. Experimental results demonstrate that the proposed approach is effective, decreasing the equal error rate (EER) of palmprint recognition from 0.653% to 0.627%.

3D Palmprint recognition using Tan and Triggs normalization technique and GIST descriptors

The 3D palmprint biometric recognition system is being considered as a very promising way to address the limitations of 2D palmprint recognition systems. This paper introduces a novel method for the 3D palmprint recognition based on the Tan and Triggs normalization technique (TT) for the preprocessing data and GIST descriptor for feature extraction. The TT technique can effectively and efficiently eliminate not only the low frequencies containing the undesirable effects of shadows but also high frequencies containing aliasing and noise in 3D palmprint. The holistic feature extraction method has been employed to attain salient characteristics. Finally, for the features matching the cosine Mahalanobis distance has been used for 3D palmprint recognition. The proposed system has been evaluated on publicly available 3D palmprint database of 8000 samples. Experimental analyses show that the proposed method can functionally eradicate the effect of uneven illumination and greatly improve the performance of the recognition system. Moreover, experimental results demonstrate that our method is capable of competing with many existing state-of-the-art 3D palmprint recognition techniques as well as outperforming many others.

Robust palmprint identification using efficient enhancement and two‐stage matching technique

Palmprint-based human authentication has shown great potential for civil, forensic, and corporate security applications in recent years. Palmprint recognition systems suffer because of large palmprint sizes and the presence of a large number of creases and erroneous minutiae that make the enhancement and matching phases a challenge. In this study, a novel approach is presented based on efficient enhancement and a two-stage matching technique that demonstrates highly accurate identification results. The enhancement approach extracts minutia features from high-quality regions based on local ridge characteristics. The selected minutiae are then matched using a two-stage local and global minutiae neighbour-based matching technique. To demonstrate the performance of the proposed technique, comparisons with open-source algorithms are made based on equal error rate and detection error trade-off graph. The results confirm the efficacy of proposed palmprint enhancement and identification technique.

3D palmprint recognition using complete block wise descriptor

Recent years have witnessed a growing interesting in developing automatic 3D palmprint recognition systems. The key problem of 3D palmprint recognition is to contrive an effective representation. In this paper, we propose a 3D palmprint recognition method by completely fusing of multiple block wise features. First, we propose a shape index multi-direction binary code strategy to extract 2D texture-level features of 3D palmprint. Then, we use the compact surface type binary code scheme to characterize 3D structure-level features of 3D palmprint. Finally, we integrate them together to construct the complete block wise descriptor for 3D palmprint recognition. Experiments conducted on the public available 3D palmprint database validate the effectiveness of our proposed method.

PalmNet: Gabor-PCA Convolutional Networks for Touchless Palmprint Recognition

Touchless palmprint recognition systems enable high-accuracy recognition of individuals through less-constrained and highly usable procedures that do not require the contact of the palm with a surface. To perform this recognition, methods based on local texture descriptors and convolutional neural networks (CNNs) are currently used to extract highly discriminative features while compensating for variations in scale, rotation, and illumination in biometric samples. In particular, the main advantage of CNN-based methods is their ability to adapt to biometric samples captured with heterogeneous devices. However, the current methods rely on either supervised training algorithms, which require class labels (e.g., the identities of the individuals) during the training phase, or filters pretrained on general-purpose databases, which may not be specifically suitable for palmprint data. To achieve a high-recognition accuracy with touchless palmprint samples captured using different devices while neither requiring class labels for training nor using pretrained filters, we introduce PalmNet, which is a novel CNN that uses a newly developed method to tune palmprint-specific filters through an unsupervised procedure based on Gabor responses and principal component analysis (PCA), not requiring class labels during training. PalmNet is a new method of applying Gabor filters in a CNN and is designed to extract highly discriminative palmprint-specific descriptors and to adapt to heterogeneous databases. We validated the innovative PalmNet on several palmprint databases captured using different touchless acquisition procedures and heterogeneous devices, and in all cases, a recognition accuracy greater than that of the current methods in this paper was obtained.

Palmprint identification performance improvement via patch-based binarized statistical image features

In the last few years, most works on palmprint recognition systems have been focused on developing a practical system that should have high performance in term of recognition accuracy, matching speed, and storage requirement. However, they have certain shortcomings, such as long computational time and sensitiveness to translation, illumination, and rotation. To handle these limitations, we present a simple and effective scheme to produce a meaningful local palmprint representation called patch binarized statistical image features descriptor (PBSIFD) for palmprint identification. The PBSIFD representation significantly exploits the power of the BSIF texture descriptor. In addition, the reduced version of PBSIFD called RPBSIFD is also obtained using whitened linear discriminant analysis. The proposed schemes are successfully applied to four widely used palmprint databases, including PolyU2D, PolyU2D/3D, IITD, and CASIA, and they are compared with recent approaches. It is shown that they outperform the existing methods.

Histogram of gradient and binarized statistical image features of wavelet subband-based palmprint features extraction

Palmprint recognition systems are dependent on feature extraction. A method of feature extraction using higher discrimination information was developed to characterize palmprint images. In this method, two individual feature extraction techniques are applied to a discrete wavelet transform of a palmprint image, and their outputs are fused. The two techniques used in the fusion are the histogram of gradient and the binarized statistical image features. They are then evaluated using an extreme learning machine classifier before selecting a feature based on principal component analysis. Three palmprint databases, the Hong Kong Polytechnic University (PolyU) Multispectral Palmprint Database, Hong Kong PolyU Palmprint Database II, and the Delhi Touchless (IIDT) Palmprint Database, are used in this study. The study shows that our method effectively identifies and verifies palmprints and outperforms other methods based on feature extraction.

Stereo-based palmprint recognition in various 3D postures

In order to increase performance in palmprint recognition systems, various devices are normally used to restrict the movement of the hand. These can cause problems, especially for those users with physical disabilities. They also cause significant hygiene problems in multi-user systems. Recently, studies on palmprint recognition systems have progressed towards the development of unconstrained, contactless and unrestricted background techniques. The most common problem encountered in these studies is the alignment arising from the free movement of the hand. Despite 3D hand-acquisition devices which offer extra recognition features to overcome this problem, the applicability of these devices is low because of their increased cost. In this study, a stereo camera was proposed. Although due to matching problems, it is difficult to achieve precise, distinct feature extraction in the unrestricted 3D environment used for palmprint recognition, the orientation of the hand in 3D space can be determined by obtaining depth information. In this study, the depth information was extracted by using the binocular stereo approach. First, the orientation of the hand was estimated by fitting a surface model associated with the eigenvectors of the depth information. Pose correction was then accomplished by establishing a relationship between the orientation and the images. The pose correction greatly relieved the perspective distortion that usually occurs within the various poses of the hands. Next, the region of interest was determined by performing segmentation on the corrected images using the Active Appearance Model (AAM). The palmprint features were then extracted via Gabor-based Kernel Fisher Discriminant Analysis. In order to demonstrate the performance of the proposed approach, a new dataset was compiled from stereo images within various scenarios collected from 138 different individuals. As a result of these experimental studies, the EER values, especially on the images captured from different hand orientations in 3D, were reduced from around 14–0.75%. With the help of this suggested approach, the palmprint recognition system was transformed into a more portable form by removing the closed-box mechanisms and equipment restricting movement of the hand. This system can automatically perform pose estimation, hand segmentation and recognition processes without any special intervention.

Palmprint Recognition Systems based-on Backpropagation Neural Network and Euclidean Distance using Principal Components Analysis (PCA) Feature Extraction

Palmprint recognition system has been one promising biometric system used in Presence System. There are some methods to recognize the individual palmprint as well as to extract its feature. In this research, two recognition methods are compared, i.e., backpropagation neural network and similarity measure using Euclidean distance. While, for feature extraction, we implemented Principal Components Analysis (PCA) method. From the research, it can be concluded that from test results, the best recognition using backpropogation neural networks is 93.33% which is reached when parameters used are: 100 principal components, 1 hidden layer, and 75 neurons. While, implementation of similarity measure using Euclidean distance, the best recognition rate is 96.67% which is reached when 75 principal components are used. When considering the time consumed in recognition, the Euclidean distance gives the better result, i.e. 17.09 seconds, while using backpropagation neural network with 75 neurons, time consumed is 425 seconds. Therefore, from this research, recognition implementation combining both PCA and Euclidean distance are more suggested rather than using combination of PCA and backpropagation neural network.

3D palmprint recognition using shape index representation and fragile bits

Recent years have witnessed a growing interesting in developing automatic palmprint recognition methods. Most of the previous works have concentrated on two dimensional (2D) palmprint recognition in the past decade. However, the shape information is lost in 2D plamprint images. What’s more, 2D plamprint recognition is not robust enough in practice since its data could be easily counterfeited or contaminated by noise. Consequently, three dimensional (3D) palmprint recognition is treated as an important alternative road to both enhance the performance and robustness of current available palmprint recognition systems. In this paper, we first explore geometrical information of 3D palmprint data by employing shape index formulation, from which Gabor wavelet features are then extracted. Furthermore, we first discover that by incorporating fragile bits information, the performance of coding strategy related 3D recognition method can be further improved. Experiments conducted on the public available 3D plamprint database validate that our method can obtain the highest recognition performance among the state-of-the-art methods estimated.

Three-dimensional ultrasound palmprint recognition using curvature methods

Palmprint recognition systems that use three-dimensional (3-D) information of the palm surface are the most recently explored techniques to overcome some two-dimensional palmprint difficulties. These techniques are based on light structural imaging. In this work, a 3-D ultrasound palmprint recognition system is proposed and evaluated. Volumetric images of a region of the human hand are obtained by moving an ultrasound linear array along its elevation direction and one by one acquiring a number of B-mode images, which are then grouped in a 3-D matrix. The acquisition time was contained in about 5 s. Much information that can be exploited for 3-D palmprint recognition is extracted from the ultrasound volumetric images, including palm curvature and other under-skin information as the depth of the various traits. The recognition procedure developed in this work is based on the analysis of the principal curvatures of palm surface, i.e., mean curvature image, Gaussian curvature image, and surface type. The proposed method is evaluated by performing verification and identification experiments. Preliminary results have shown that the proposed system exhibits an acceptable recognition rate. Further possible improvements of the proposed technique are finally highlighted and discussed.

Concavity-orientation coding for palmprint recognition

Efficient feature extraction strategies play an important role in palmprint recognition systems. Among various feature extraction methods, orientation methods such as Competitive Code and Half Orientation Code are the baseline ones. They encode responses of a bank of orientational filters into a binary representation and can match a test palmprint sample in real-time with a relatively good accuracy. However, they use the orientation information based upon this idea that palmprints encompass only straight lines with different orientations, whereas in reality, the majority of palm's lines are curved. This observation naturally brings the idea that the concavity and orientation features as different aspects of palmprints curves might provide more reliable and discriminative representations in palmprint recognition. Motivated by this idea, in this work we investigate the use of the concavity feature in different orientations for palmprint recognition. The experimental results, which are applied on PolyU II, 2D/3D PolyU, and blue and near infrared range images from Multispectral PolyU palmprint databases prove the efficiency of this idea compared to other coding-based methods.