Palmprint Biometrics Research Articles

Explainable multi-layer COSFIRE filters robust to corruptions and boundary attack with application to retina and palmprint biometrics

We propose a novel and versatile computational approach, based on hierarchical COSFIRE filters, that addresses the challenge of explainable retina and palmprint recognition for automatic person identification. Unlike traditional systems that treat these biometrics separately, our method offers a unified solution, leveraging COSFIRE filters’ trainable nature for enhanced selectivity and robustness, while exhibiting explainability and resilience to decision-based black-box adversarial attack and partial matching. COSFIRE filters are trainable, in that their selectivity can be determined with a one-shot learning step. In practice, we configure a COSFIRE filter that is selective for the mutual spatial arrangement of a set of automatically selected keypoints of each retina or palmprint reference image. A query image is then processed by all COSFIRE filters and it is classified with the reference image that was used to configure the COSFIRE filter that gives the strongest similarity score. Our approach, tested on the VARIA and RIDB retina datasets and the IITD palmprint dataset, achieved state-of-the-art results, including perfect classification for retina datasets and a 97.54% accuracy for the palmprint dataset. It proved robust in partial matching tests, achieving over 94% accuracy with 80% image visibility and over 97% with 90% visibility, demonstrating effectiveness with incomplete biometric data. Furthermore, while effectively resisting a decision-based black-box adversarial attack and impervious to imperceptible adversarial images, it is only susceptible to highly perceptible adversarial images with severe noise, which pose minimal concern as they can be easily detected through histogram analysis in preprocessing. In principle, the proposed learning-free hierarchical COSFIRE filters are applicable to any application that requires the identification of certain spatial arrangements of moderately complex features, such as bifurcations and crossovers. Moreover, the selectivity of COSFIRE filters is highly intuitive; and therefore, they provide an explainable solution.

Enhanced Multitask Learning for Hash Code Generation of Palmprint Biometrics.

This paper presents a novel multitask learning framework for palmprint biometrics, which optimizes classification and hashing branches jointly. The classification branch within our framework facilitates the concurrent execution of three distinct tasks: identity recognition and classification of soft biometrics, encompassing gender and chirality. On the other hand, the hashing branch enables the generation of palmprint hash codes, optimizing for minimal storage as templates and efficient matching. The hashing branch derives the complementary information from these tasks by amalgamating knowledge acquired from the classification branch. This approach leads to superior overall performance compared to individual tasks in isolation. To enhance the effectiveness of multitask learning, two additional modules, an attention mechanism module and a customized gate control module, are introduced. These modules are vital in allocating higher weights to crucial channels and facilitating task-specific expert knowledge integration. Furthermore, an automatic weight adjustment module is incorporated to optimize the learning process further. This module fine-tunes the weights assigned to different tasks, improving performance. Integrating the three modules above has shown promising accuracies across various classification tasks and has notably improved authentication accuracy. The extensive experimental results validate the efficacy of our proposed framework.

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)

Accurate Biometric Palm Print Recognition Using ResNet50 algorithm Over X Gradient Boosting Algorithm

The aim of this research is to enhance the accuracy of biometric palm print identification by using the Novel ResNet50 Algorithm as compared to the X Gradient Boosting. Materials and Methods: In this study, the ResNet50 and X Gradient Boosting algorithms were compared using a sample size of 10 for each algorithm, resulting in a total sample size of 20. The comparison was carried out with a G Power of 0.8 and a confidence interval (CI) of 95% to ensure statistical significance. For this study the Birjand University Mobile Palmprint Database (BMPD) dataset was collected from the Kaggle repository, which includes a total of 1640 images containing both left and right-hand palmprints. Result: According to the results, the ResNet50 algorithm achieved a higher accuracy rate (94.7%) compared to the X Gradient Boosting algorithm (92.4%) in identifying and measuring the images. The statistical analysis indicated a significant difference between the Novel ResNet50 algorithm and X Gradient Boosting, with a pvalue of 0.003 (Independent sample T-test p<0.05). This suggests that the ResNet50 algorithm outperformed the X Gradient Boosting algorithm in this experiment. According to the study’s findings, ResNet50 is more effective in accurately identifying biometric palm prints compared to X Gradient Boosting.

Contactless palmprint biometrics using DeepNet with dedicated assistant layers

Contactless palmprint biometrics using DeepNet with dedicated assistant layers

Efficient palmprint biometric identification systems using deep learning and feature selection methods

Over the past two decades, several studies have paid great attention to biometric palmprint recognition. Recently, most methods in literature adopted deep learning due to their high recognition accuracy and the capability to adapt with different acquisition palmprint images. However, high-dimensional data with a large number of uncorrelated and redundant features remain a challenge due to computational complexity issues. Feature selection is a process of selecting a subset of relevant features, which aims to decrease the dimensionality, reduce the running time, and improve the accuracy. In this paper, we propose efficient unimodal and multimodal biometric systems based on deep learning and feature selection. Our approach called simplified PalmNet–Gabor concentrates on the improvement of the PalmNet for fast recognition of multispectral and contactless palmprint images. Therefore, we used Log-Gabor filters in the preprocessing to increase the contrast of palmprint features. Then, we reduced the number of features using feature selection and dimensionality reduction procedures. For the multimodal system, we fused modalities at the matching score level to improve system performance. The proposed method effectively improves the accuracy of the PalmNet and reduces the number of features as well the computational time. We validated the proposed method on four public palmprint databases, two multispectral databases, CASIA and PolyU, and two contactless databases, Tongji and PolyU 2D/3D. Experiments show that our approach achieves a high recognition rate while using a substantially lower number of features.

Contact-Less Palmprint Biometric for Securing DSP Coprocessors Used in CE Systems

This paper presents a novel contact-less palmprint biometric hardware security approach for securing DSP based coprocessors. The proposed approach is capable of generating secret biometric palmprint constraints that are embedded in DSP designs used in consumer electronics systems to detect counterfeited versions. Any DSP based intellectual property (IP) core can be embedded with proposed palmprint signature to distinguish between authentic and its fake versions. In the proposed approach, the authentic biometric palmprint is first converted into its equivalent palmprint signature representing digital template based on palmprint feature set, followed by embedding palmprint signature’s digital template into the design in the form of secret biometric constraints. The biometric palmprint constraints generated through the proposed approach is non-replicable and non-vulnerable compared to other signature based scheme such as hardware steganography and hardware watermarking. The proposed approach exploits several security features such as: palmprint nodal points, palmprint features, palmprint feature ordering, concatenation mechanism of features etc to generate a robust palmprint hardware security constraint. The results of the proposed approach indicated stronger probability of co-incidence and stronger tamper tolerance ability than recent related works.

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.

Smart Door System Prototype with a Control Based on Biometric Palmprint and the Internet of Things

Safety is important in terms of comfort and security in a home, because a house is a place where people live and store valuable items. However, conventional keys that are widely used today are still vulnerable to break-ins. Therefore, a reliable and qualified security system is needed to safeguard a person’s home. To overcome this problem, this study proposed a security system to access the door of a house to maintain the home’s security. This door access security system is based on Palmprint, in which a certain line on a person’s palm is used to open the door and gain access into the house. Palmprint is supported by the Internet of Things system so that residents can remotely control and access the door lock. The use of biometric authentication will replace a conventional key so that the door will only be opened if the lines on the palm of the hand can be identified in accordance with the data registered by the owner. If a burglary occurs or if the lock is forced, the system will notify the residents of the house through their Palmprint application. The results show that the network system works optimally in the Line of Sight scenario with a distance of 5 meters, and it produces a data delay measuring 0.255436 s; moreover, a throughput of 8825,933 bytes/s is obtained from end-to-end testing of the total system in the 5 meter Line of Sight scenario with a total average delay of 0.570077 s and an average throughput of 11776.27 bytes/s.

Biometric Palmprint Verification: A Dynamical System Approach

Most of the existing techniques for palmprint recognition rely on metrics, typically based on static functions, which evaluate the distance between a pair of features. In this paper, we propose a new technique for palmprint verification based on a dynamical system approach for principal palm lines matching. The proposed dynamic algorithm is recursive and involves a positive linear dynamical system, whose evolution depends on the matching level between the two input images. In a preprocessing phase, the procedure iteratively erodes both of the images to be compared, by eliminating points in each image that do not have enough close neighboring points both in the image itself and the comparison image. As a result of the iterations, only the points that have enough neighboring points in both the image itself and in the comparison image can survive. Thus, the output of the dynamical system converges either to zero, when a deep mismatch exists between the two images, or to a high value, when a good matching is observed. The results, in terms of verification, are in line with the state-of-the-art results in the current literature. The main advantage of the approach is its robustness when dealing with low-resolution and noisy images. The impact of noise (e.g., salt and pepper noise) is effectively reduced: images corrupted with such noise are easily recognized, while a randomly generated image is rejected even when compared with itself.

Feature Extraction of Iris and Palmprint Biometric System using Dwt

Much work is done on Iris Recognition, since few years. Many cases discussed about performance in view of image capturing and recognition. Daugman work is the most important related to iris biometric in early research. It is fair to say, it is base model for iris biometric. Almost the available iris systems are based on this work. A palm print is image of the palm area of a hand. It is either an image taken online or offline. It is one of the most familiar and promising biometric model for personal identity verification. It is tough task to differentiate lines and wrinkles without explicit definition. depends on the thickness and position of some key points we can define principal lines. In our work, we are taken the principal line magnitude is less than or equal to 1. we cannot consider broken lines. If it is the case broken point is treat as last point.

Lightweight Verification Schema for Image-Based Palmprint Biometric Systems

Palmprint biometrics is a promising modality that enables efficient human identification, also in a mobile scenario. In this paper, a novel approach to feature extraction for palmprint verification is presented. The features are extracted from hand geometry and palmprint texture and fused. The use of a fusion of features facilitates obtaining a higher accuracy and, at the same time, provides more robustness to intrusive factors like illumination, variation, or noise. The major contribution of this paper is the proposition and evaluation of a lightweight verification schema for biometric systems that improves the accuracy without increasing computational complexity which is a necessary requirement in real-life scenarios.

Composite Feature Extraction and Classification for Fusion of Palm-Print and Iris Biometric Traits

Palm-print and iris biometric traits fusion are implemented in this paper. The region of interest (ROI) of a palm is extracted by using the valley detection algorithm and the ROI of an iris is extracted based on the neighbor-pixels value algorithm (NPVA). Statistical local binary pattern (SLBP) is applied to extract the local features of palm and iris. For enhancing the palm features, a combination of histogram of oriented gradient (HOG) and discrete cosine transform (DCT) is applied. Gabor-Zernike moment is used to extract the iris features. This experimentation was carried out in two modes: verification and identification. The Euclidean distance is used in the verification system. In the identification system, the fuzzy-based classifier was proposed along with built-in classification functions in MATLAB. CASIA datasets of palm and iris were used in this research work. The proposed system accuracy was found to be satisfactory.

Superpixel-based Zernike moments for palm-print recognition

In the contemporary period, significant attention has been focused on the prospects of innovative personal recognition methods based on palm-print biometrics. However, diminished local consistency and interference from noise are only some of the obstacles that hinder the most common methods of palm-print imaging such as the grey texture and other low-level of the palm. Nevertheless, the development of the process and tackling of the obstacles faced have a potential solution in the form of high-level characteristic imaging for palm-print identification. In this study, Zernike moments are used for acquiring superpixel features that are spiral scanned images, which is an innovative recognition method. By using the extreme learning machine, the inter- and intra-similarities of the palm-print feature maps are determined. Our experiments yield good results with an accuracy rate of 97.52 and an equal error rate of 1.47% on the palm-print PolyU database.

Design of Low-Cost Personal Identification System That Uses Combined Palm Vein and Palmprint Biometric Features

This paper proposes a low-cost personal identification system that uses the combined palm vein and palmprint biometric features. The system consists of near-infrared and visible light-emitting diode (LED) arrays, a low-cost visual sensor, a Xilinx chip, and other components. A real-time image quality assessment (IQA) method for the combined palm vein and palmprint biometric features is also proposed. Two types of the LED with central frequency spectra of 890 and 680 nm are used to capture the palm vein and palmprint, respectively. The adaptive feedback control of the diode brightness is in accordance with the image quality assessed by the combined 2D entropy and local 2D entropy. The palm vein and palmprint images are acquired nearly simultaneously, and each acquired image undergoes a few preprocessing steps for extraction of the vein and print patterns. We use an image-level wavelet-based fusion strategy to reduce image storage requirement for the embedded platform and implement a complex wavelet-based fusion strategy for the PC platform. A deep scattering convolutional network is applied for extracting the features of the fused images, and a multi-class support vector machine is used for training and recognition. Characteristics of some vision-based personal identification systems are discussed. The proposed real-time IQA method with fusion strategy and feature extraction algorithm in our prototype system has substantially less operational requirements than that of the previous fusion strategies. It also demands less memory and yields lower equal error rate than the classical feature extraction algorithms.

39‐3: Printed Organic Photodetector Arrays and their use in Palmprint Scanners

Using flat‐panel compatible processes we realized 6x8 cm, 200‐ppi active‐matrix imagers with solution‐processed, ultrathin organic photodiodes and demonstrate its performance in a biometric palmprint detector.

Spoof detection on face and palmprint biometrics

Spoofing attacks made by non-real images are a major concern to biometric systems. This paper presents a novel solution for distinguishing between live and forged identities using the fusion of texture-based methods and image quality assessment measures. In our approach, we used LBP and HOG texture descriptors to extract texture information of an image. Additionally, feature space of seven full-reference complementary image quality measures is considered including peak signal-to-noise ratio, structural similarity, mean-squared error, normalized cross-correlation, maximum difference, normalized absolute error and average difference. We built a palmprint spoof database made by printed palmprint photograph of PolyU palmprint database using camera. Experimental results on three public-domain face spoof databases (Idiap Print-Attack, Replay-Attack and MSU MFSD) and palmprint spoof database show that the proposed solution is effective in face and palmprint spoof detection.

A Novel Line-Scan Palmprint Acquisition System

Biometric recognition systems have been widely used globally. However, one effective and highly accurate biometric authentication method, palmprint recognition, has not been popularly applied as it should have been, which could be due to the lack of small, flexible and user-friendly acquisition systems. To expand the use of palmprint biometrics, we propose a novel palmprint acquisition system based on the line-scan image sensor. The proposed system consists of a customized and highly integrated line-scan sensor, a self-adaptive synchronizing unit, and a field-programmable gate array controller with a cross-platform interface. The volume of the proposed system is over 94% smaller than the volume of existing palmprint systems, without compromising its verification performance. The verification performance of the proposed system was tested on a database of 8000 samples collected from 250 people, and the equal error rate is 0.048%, which is comparable to the best area camera-based systems.

Feature selection for the fusion of face and palmprint biometrics

Multimodal biometric systems aim to improve the recognition accuracy by minimizing the limitations of unimodal systems. In this paper, different fusion schemes based on feature-level and match score-level fusion are employed to provide a robust recognition system. The proposed method presents a multimodal approach based on face–palmprint biometric systems by match score-level fusion technique. Local binary patterns are performed as local feature extractor to obtain efficient texture descriptor. Feature selection is performed using backtracking search algorithm to select an optimal subset of face and palmprint extracted features. Hence, computation time and feature dimension are considerably reduced while obtaining the higher level of performance. Then, match score-level fusion is performed to show the effectiveness and accuracy of the proposed method. In score-level fusion, face and palmprint scores are normalized using tanh normalization and matching scores of individual classifiers are fused using sum rule method. The experimental results are tested on a developed virtual multimodal database combining FERET face and PolyU palmprint databases. The results demonstrate a significant improvement compared with unimodal identifiers, and the proposed approach significantly outperforms other face–palmprint multimodal systems with a recognition accuracy of 99.17 %. Additionally, the proposed approach is compared with the state-of-the-art methods.

Efficient Multimodal Biometric System Based on Feature Level Fusion of Palmprint and Finger Vein

Biometric authentication is playing a vital role in providing security and privacy. This paper presents a contemporary approach for identifying an individual using the multimodal biometrics has great demands to overcome the issues involved in single trait system. Finger vein and palmprint biometric is a promising technology, now-a-days widely used because of its important features such as resistant to criminal tampering, high accuracy, ease of feature extraction and fast authentication speed. The features extracted from the preprocessed finger vein and palmprint images using Contourlet Transform reduce the overall dimensionality and computational complexity. Fusion at feature level utilizes the fused feature vectors of finger vein and palmprint using the Discrete Stationary Wavelet Transform (DSWT) to improve the overall performance of the multimodal biometric system. Integrating the biometric traits increases the robustness of the person identification and reduces fraudulent access. Experimental results based on homologous database demonstrate that the proposed system is very efficient to reduce the False Rejection Rate (FRR) and False Acceptance Rate (FAR).