Palmprint Identification System 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.

Palmprint recognition system using VR-LBP and KAZE features for better recognition accuracy

The palmprint recognition system has gained significant attention in security and law enforcement due to its unique features, such as principle lines, ridges, and wrinkles. However, many existing methods for extracting these features have limited accuracy, especially when the image illumination varies or the size of the processed pixels increases. Previous studies have shown that the local binary patterns (LBP) algorithm is effective for palmprint recognition due to the rich texture characteristics of a palmprint. In this paper, we propose a new technique for a robust contact-based palmprint identification system using vertical-LBP and KAZE feature detection. Our technique aims to improve recognition accuracy by using KAZE, which is a nonlinear diffusion approach that extracts nonlinear features from the evolution of the illuminance of an image. We also utilize principal component analysis (PCA) to reduce the dimensionality of the generated descriptor vector elements. The proposed method was tested on the PolyU database and achieved recognition accuracy of 99.7%.

A novel indexing algorithm for latent palmprints leveraging minutiae and orientation field

Latent palmprints represent crucial forensic evidence in criminal investigations, necessitating their storage in governmental databases. The identification of corresponding palmprints within large-scale databases using an automated palmprint identification system (APIS) is time-consuming and computationally intensive. To address this challenge, this paper introduces an innovative approach: delineating the region of interest (ROI) for palmprint segmentation and presenting a novel indexing algorithm founded on minutiae and the orientation field (OF). Additionally, a novel feature vector is proposed, leveraging minutiae triplets and ellipse properties, marking the pioneering algorithm to consider minutiae importance in palmprint indexing. Significantly, an improved version of an existing palmprint indexing algorithm tailored for latent palmprints is introduced. The study demonstrates the indexing and retrieval of both our feature vectors and those obtained by the improved palmprint indexing algorithm, using two clustering algorithms and locality-sensitive hashing (LSH). The method's robustness is evaluated across three diverse databases with extensive palmprint records. The experimental results underscore the superior performance of our approach compared to current state-of-the-art algorithms.

An effective hyperspectral palmprint identification system based on deep learning and band selection approach

An effective hyperspectral palmprint identification system based on deep learning and band selection approach

Orientation Field Code Hashing: A Novel Method for Fast Palmprint Identification

For a large-scale palmprint identification system, it is necessary to speed up the identification process to reduce the response time and also to have a high rate of identification accuracy. In this paper, we propose a novel hashing-based technique called orientation field code hashing for fast palmprint identification. By investigating hashing-based algorithms, we first propose a double-orientation encoding method to eliminate the instability of orientation codes and make the orientation codes more reasonable. Secondly, we propose a window-based feature measurement for rapid searching of the target. We explore the influence of parameters related to hashing-based palmprint identification. We have carried out a number of experiments on the Hong Kong PolyU large-scale database and the CASIA palmprint database plus a synthetic database. The results show that on the Hong Kong PolyU large-scale database, the proposed method is about 1.5 times faster than the state-of-the-art ones, while achieves the comparable identification accuracy. On the CASIA database plus the synthetic database, the proposed method also achieves a better performance on identification speed.

A multi-instance multi-sample palmprint identification system

<span>The multibiometric recognition system considered more reliable than the unimodal biometric recognition system due to the addition of an extra information that increases the discrimination between the classes. In this paper, a multi-sample multi-instance biometric recognition system is proposed. The aim of the proposed system is to increase the robustness of the identification. the proposed system also addresses the overfitting to the train samples problem of a feature extraction algorithm, named 2-Dimensional Linear Discriminant analysis (2D-LDA). The samples in the proposed method are bootstrapped and the 2D-LDA performed on each group during the offline phase. While in the online phase, the tested class will be transformed into subspaces using different eigenvectors that obtained from different samplings, and the results matched with templates in the corresponding subspace. To evaluate the proposed method, two palmprint databases are used which are IIT Delhi Touchless Palmprint Database and PolyU palmprint database, and different rank-level fusion algorithms are investigated. The results of the proposed method show improvement in the identification rate.</span>

Massively parallel palmprint identification system using GPU

Automated human authentication is becoming increasingly important in today’s world due to increased need of security and surveillance applications deployed in almost all premises and installations. In this regard, palmprint biometric based identification has gained a lot of attention in recent years. However, due to large size of palmprint images and presence of principal lines, wrinkles, creases, and other noises, there are large number of inaccurate minutiae present. The computational requirement of palmprint identification is also quite large and it takes a lot of time to find identity of a palmprint in large database. In this study, a novel palmprint identification solution has been proposed that increases the accuracy of minutia detection based on improved frequency estimation and a novel region-quality based minutia extraction algorithm. Furthermore, a novel, efficient and highly accurate minutiae based encoding and matching algorithm is proposed that is designed to achieve maximum parallelism, and it is further accelerated using graphical processing unit. The results of the proposed palmprint identification demonstrate high accuracy and much faster identification speeds in comparison with current state of the art. Therefore, it can be considered as a robust, efficient and practical solution for palmprint based identification systems.

Multi-Algorithm of Palmprint Recognition System Based on Fusion of Local Binary Pattern and Two-Dimensional Locality Preserving Projection

This work based on fusion of multi-algorithm used for palmprint identification system. This piece of work is primary addressing different mechanism like Competitive Valley Hand Detection methods (CHVD) which used for extract Region of Interest (ROI). While in feature extraction the work was divided into three scenarios based on feature extraction like Local Binary Pattern (LBP), Two-Dimensional Locality Preserving Projection (2DLPP) and fusion of LBP+2DLPP.The experimental results show that fusion of LBP and 2DLPP give the best result with high accuracy reach to 98.55% which improve by 1.22% and 2.85% for LBP and 2DLPP respectively when they are applied separately.

Contactless palm landmark detection and localization on mobile devices

Palmprint recognition as a novel biometric identification method for contactless mobile devices has been received substantial attentions in recent years. Palm landmark detection is one of the key technologies of palmprint identification and verification system. However, the differences of hand positions, complex backgrounds and various lighting conditions in unrestrained environment with low-resolution cameras make palm landmark detection in the wild difficult. In this paper, we proposed a new palm landmark detection approach based on Supervised Descent Method (SDM). SDM uses the relationship between the feature representation and the position of a landmark point to build an optimization problem for palm landmark detection. The optimization target function is the distance of feature representations between current position and the ideal position of a palm landmark point. After optimization, a linear function of the position displacement and the feature representation of current landmark is obtained. The linear function can be learned from palmprint image samples with labeled landmark positions. Given an input image in detection process, the initial position of a landmark is set by the mean position of the landmark in the training set, then the optimal landmark position can be calculated iteratively using the learned linear function. The effectiveness of the proposed method is proved on a mobile phone captured palm image dataset.

Palm Print Identification Using Improved Histogram of Oriented Lines

Automatic palmprint identification has received much attention in security applications and law enforcement. The performance of a palmprint identification system is improved by means of feature extraction and classification. Feature extraction methods such as Subspace learning are highly sensitive to the rotation variances, translation and illumination in image identification. Thus, Histogram of Oriented Lines (HOL) has not obtained promising performance for palmprint recognition so far. In this paper, we propose a new descriptor of palmprint named Improved Histogram of Oriented Lines (IHOL), which is an alternative of HOL. Improved HOL is not very sensitive to changes of translation and illumination, and has the robustness against small transformations whereas the small translation and rotations make no change in histogram value adjustment of the proposed work. The experiment results show that based on IHOL, with Principal Component Analysis (PCA) subspace learning can achieve high recognition rates. The proposed method (IHOL-Cosine distance) improves 1.30% on PolyU I database, and similarly (IHOL-Euclidean distance) improves 2.36% on COEP database compared with existing HOL method.

IJARCCE - Computer and Communication Engineering

Biometric technologies are becoming inevitable in highly secure identification and personal verification solutions. With an increase in transaction frauds, security breaches the need for highly secure identification and personal verification technologies is becoming need of the hour. The major advantage of biometric system over traditional methods is that they are typically unique for each person and cannot be forged easily. Biometric use of palm prints uses ridge patterns to identify an individual. The Palm print identification system is most promising to identify an individual based on statistical properties of palm print image. Its rich features like principal lines, minutiae points, wrinkles, ridges and singular points makes it favourable to be used as one of the best biometric identification. This paper proposed the palm print identification system on GPU.

An Efficient 2D and 3D Palmprint Identification System by Jointly Using Gabor Filter Response, Wavelet Transform and Radial Basis Function

An Efficient 2D and 3D Palmprint Identification System by Jointly Using Gabor Filter Response, Wavelet Transform and Radial Basis Function

Consistency analysis on orientation features for fast and accurate palmprint identification

A typical palmprint identification system identifies a query palmprint by matching it with the templates in the database and comparing the similarity score with a pre-defined threshold. However, continual database expansion would make conventional brute force search method inefficient for large-scale palmprint identification. In this paper, we propose a consistent orientation pattern (COP) hashing method to enforce fast search. Using the orientation and response features extracted by steerable filter, we first give an analysis on the consistency of orientation features, and then introduce a method to construct COP using the consistent features. Because the COP is very stable across the samples of the same subject, the COP hashing method can find the target template quickly and thus can lead to early termination of the searching process. Experiments have been carried out on several real and synthetic databases, and the results show that the COP hashing method can accelerate the identification process by more than an order of magnitude, and can achieve comparable identification accuracy while compared with the state-of-the-art approaches.

FBI intros national palm print search

The US FBI deployed its Next Generation Identification (NGI) Increment 3 in May 2013, improving latent fingerprint search accuracy and introducing a nationwide palm print identification system to help solve cold cases and improve crime solving capabilities.

Palmprint enhancement using recursive histogram equalisation

Palmprint identification system is one of the most powerful personal identification systems in recent years. In order to achieve high identification accuracy, all parts of the palmprint are needed to be enhanced. Histogram equalisation is a very popular image enhancing technique. A novel histogram equalisation technique, called recursive\\ histogram equalisation, for brightness preservation and image contrast enhancement, is put forward in this paper. The essence of proposed algorithm is to decompose an input histogram into two or more sub-histograms recursively based on its mean, change the sub-histograms through a weighting process based on a normalised power law function and then equalise the weighted sub-histograms independently. Experiments show that our method preserves the mean brightness of a given image, enhances the contrast and produces more natural looking images than the other histogram equalisation methods.

Fast palmprint identification with multiple templates per subject

Palmprint identification system commonly stores multiple templates for each subject to improve the identification accuracy. The system then recognizes a query palmprint image by searching for its nearest neighbor from all of the templates. When applied on moderate or large scale identification system, it is often necessary to speed up this process. In this paper, to speed up the identification process, we propose to utilize the intrinsic characteristics of the templates of each subject to build a tree, and then perform fast nearest neighbor searching with assistance of the tree structure. Furthermore, we propose a novel method to generate the ‘virtual’ template from all the real templates of each subject. The tree constructed by the virtual template and the real templates can further speed up the identification process. Two representative coding-based methods, competitive code and ordinal code, are adopted to demonstrate the effectiveness of our proposed strategies. Using the Hong Kong PolyU palmprint database (version 2) and a large scale palmprint database, our experimental results show that the proposed method searches for nearest neighbors faster than brute force searching, and the speedup becomes larger when there are more templates per subject in the database. Results also show that our method is very promising for embedded system based moderate scale and PC based large scale identification systems.

High Performance Palmprint Identification System Based On Two Dimensional Gabor

The palmprint is a new recognition method in physiological biometrics. In palmprint region of interest (ROI), the segmentation and feature extraction are two important issues. The main problem in palmprint recognition system is how to extract the region of interest (ROI) and the features of palmprint. This paper introduces two steps in center of mass moment and the application of method for ROI segmentation and then to apply the Gabor two dimensional (2D) filters to obtain palm code as palmprint feature vector. Normalized Hamming distance is used to measure the similarity degrees of two feature vectors of palmprint. The system has been tested by using database 1000 palmprint images which was generated from 5 groups of samples from 200 persons selected randomly. Experiment results show that this system can achieve a high performance with success rate about 98.7% (FRR=1.1667%, FAR=0.1111%, T=0.376).

Palmprint matching algorithm based on-àtrous-Contourlet transform and invariant moments

In order to improve the speed and accuracy of palmprint identification system and overcome the weakness of Contourlet transform in dealing with high-dimensional signals,a novel identification algorithm of palmprint feature was proposed.Firstly,the atrous-Contourlet transformation for palmprint image was decomposed to obtain high frequency coefficients and low frequency coefficients in the different sub-band of different direction.Then,different feature weighting coefficients according to the statistical characteristics extracted from different frequency domains were chosen,which were calculated to get some new invariant moments vectors.Finally,the palmprint identification was achieved.The experimental results show that the method here is more effective in matching than wavelet moment algorithm,Hu's invariant moment algorithm and Contourlet algorithm.

Competitive code–based fast palmprint identification using a set of cover trees

A palmprint identification system recognizes a query palmprint image by searching for its nearest neighbor from among all the templates in a database. When applied on a large-scale identification system, it is often necessary to speed up the nearest-neighbor searching process. We use competitive code, which has very fast feature extraction and matching speed, for palmprint identification. To speed up the identification process, we extend the cover tree method and propose to use a set of cover trees to facilitate the fast and accurate nearest-neighbor searching. We can use the cover tree method because, as we show, the angular distance used in competitive code can be decomposed into a set of metrics. Using the Hong Kong PolyU palmprint database (version 2) and a large-scale palmprint database, our experimental results show that the proposed method searches for nearest neighbors faster than brute force searching.

Real-time palmprint acquisition system design

Capturing high quality palmprint images in a very short time period is a fundamental step in palmprint research. While a palmprint scanner capable of this function is especially important for developing a real-time online palmprint identification system, limited research effort has been put into designing and implementing palmprint acquisition systems. In this paper, a novel palmprint acquisition system is presented, which is designed for various civilian applications such as access control, ATM etc. The novel contactless design on the user interface prevents the distortion of the palm skin introduced by the traditional glass plate of a palm scanner. The system is designed to have the best performance at reasonable price. It has a resolution of 150 dpi that is able to obtain palmprint features including the principal lines, wrinkles and ridge texture. The proposed system provides an important step for implementing a personal identification system using palmprint. Over 9,400 palmprint images were collected by the proposed system. An online palmprint identification system was then developed based on the proposed acquisition system.