Palmprint Images Research Articles

Palm Print Recognition using Zernike Moments

ABSTRACT This paper proposes an automated system for recognizing palmprints for biometric identification of individuals. Complex Zernike moments are constructed using a set of complex polynomials which form a complete orthogonal basis set defined on the unit disc. Palmprint images are projected onto the basis set resulting in a set of complex signals. The magnitude of the complex value is computed and a scalar value is derived from it by computing the mean of the vector elements. Classification is done by subtracting the test samples from the mean of the training set. The data set consists of 80 images divided into 4 classes. Accuracy obtained is comparable to the best results reported in literature General Terms Pattern Recognition, Computer Vision Keywords Zernike moment, Palmprint recognition, Texture classification 1. INTRODUCTION Biometrics refers to automatic recognition of individuals based on their physiological and behavioral characteristics, which are nowadays used extensively for personal identification worldwide. Different physical traits like face, iris, fingerprint, palmprint, retina etc. fall under the perview of biometrics. Palmprint recognition involves identifying an individual by the principal lines, wrinkles, ridges on the surface of the palm. The basis for using palmprints lies in the fact that no two individuals have exactly the same palmprint pattern, moreover palmprints remain more or less stable throughout the lifetime and are easily obtainable using standard imaging techniques. Challenges in palmprint recognition are related to building a reliable data model from randomly oriented irregular lines that enable high amount of accuracies in security based systems and applications. This paper presents an efficient algorithm for palm print recognition by utilizing complex Zernike moments. The organization of the paper is as follows: section 2 provides an overview of the related works, section 3 outlines the proposed approach, section 4 details the experimentations done and results obtained, section 5 analyses the current work vis-a-vis contemporary works, section 6 brings up the overall conclusions and future scopes.

A DCT-Based Local Dominant Feature Extraction Algorithm for Palm-Print Recognition

In this paper, a frequency domain feature extraction algorithm for palm-print recognition is proposed, which efficiently exploits the local spatial variations in a palm-print image. The entire image is segmented into several small spatial modules and the effect of modularization in terms of the entropy content of the palm-print images has been investigated. A palm-print recognition scheme is developed based on extracting dominant spectral features from each of these local modules using a two-dimensional discrete cosine transform (2D-DCT). The proposed dominant spectral feature selection algorithm offers the advantage of having very low feature dimension, and it is capable of capturing precisely the variations in detail within the palm-print image. It is shown that because of modularization of the palm-print image, the discriminating capabilities of the proposed features are enhanced, which results in a very high within-class compactness and between-class separability of the extracted features. A principal component analysis is performed to further reduce the feature dimension. From our extensive experimentations on different palm-print databases, it is found that the performance of the proposed method in terms of recognition accuracy and computational complexity is superior to that of some of the recent methods.

Designing palmprint based recognition system using local structure tensor and force field transformation for human identification

This paper presents an efficient palmprint based automatic recognition system. It uses local structure tensor to extract features from the palmprint. Each enhanced palmprint image has been divided into sub-images and features are obtained based on local properties within the sub-image. Force field transformation is used to emphasize the texture of the palmprint and the chosen dominant orientation pixels are used for feature extraction to reduce the effect of noise. Structure tensor values of the dominant orientation pixels within a sub-image are averaged to form the tensor matrix for the sub-image. Eigen decomposition of each tensor matrix is used to generate the feature matrix. Euclidean distance between feature matrices of two palmprints has been used to make the decision on matching. The system has been tested on three databases viz. IITK, CASIA and PolyU and achieved high accuracy. It has also been compared with two best known systems using PolyU database and it has performed better than these two systems.

Fragile Bits in Palmprint Recognition

Recent years have witnessed a growing interest in developing automatic palmprint recognition methods. Among them, coding-based ones, representing the texture of a palmprint using a binary code, are most prevalent and successful. We find that not all bits in a code map generated by a specific coding scheme are equally consistent. A bit is deemed fragile if its value changes across code maps created from different images of the same palmprint. In this paper, we first analyze the fragile bits phenomenon in a state-of-the-art palmprint coding scheme, namely, binary orientation co-occurrence vector (BOCV). Then, based on our analysis, we extend BOCV to E-BOCV by incorporating fragile bits information in appropriate ways. Experiments conducted on the benchmark dataset demonstrate that E-BOCV can achieve the highest verification accuracy among all the state-of-the-art palmprint verification methods evaluated. To our knowledge, this is the first work investigating the fragile bits of coding-based palmprint recognition approaches.

Robust palmprint identification based on directional representations and compressed sensing

In this paper, we propose a novel approach for palmprint recognition, which contains two interesting components: directional representation and compressed sensing. Gabor wavelets can be well represented for biometric image for their similar characteristics to human visual system. However, these Gabor-based algorithms are not robust for image recognition under non-uniform illumination and suffer from the heavy computational burden. To improve the recognition performance under the low quality conditions with a fast operation speed, we propose novel palmprint recognition approach using directional representations. Firstly, the directional representation for palmprint appearance is obtained by the anisotropy filter, which is robust to drastic illumination changes and preserves important discriminative information. Then, the principal component analysis (PCA) is used for feature extraction to reduce the dimensions of the palmprint images. At last, based on a sparse representation on PCA feature, the compressed sensing is used to distinguish palms from different hands. Experimental results on the PolyU palmprint database show the proposed algorithm have better performance than that of the Gabor based methods.

The Study of Network Authentication Methods Based on Palmprint and Digital Watermarking

Semi-fragile digital watermarking is able to distinguish between accidental attacks and malicious tampering, thus been closely watched in terms of multimedia content authentication. From the perspective of combining digital watermarking technology and palmprint identification, using the hidden and semi-fragile features of digital watermarking and the invariance character of palm information, the two-factor network authentication can be realized. The authentication enhances network security, and it is also a strong authentication scheme. The paper put forward a network authentication method based on palmprint and digital watermarking. The digital watermarking algorithm based on logistic encryption and discrete wavelets transform (DWT). The experimental results show that the algorithm has good semi-fragile feature, it can determine the authenticity of the palmprint image, so the method increases the reliability and efficiency of user authentication.

Entropy Optimized Palmprint Enhancement Using Genetic Algorithm and Histogram Equalization

Palmprint identification is a subcategory of biometrics identification, which can be efficiently used to identify the people. Palmprint-based identification is currently a potential alternative to human identification method of a well known fingerprint-based identification. In order to achieve high identification accuracy, all components of the scanned palmprint image need to be enhanced, i.e. palmprint lines, textures and hand geometry features. Based on Histogram Equalization (HE), a contrast enhancement scheme named Adaptively Increasing Value Histogram Equalization (AIVHE) can be used as an enhancement technique. In AIVHE method, the enhancement is controlled by two user parameters beta and gamma. Furthermore, AIVHE method enhances the contrast but the detailed information in the palmprint is not preserved. In order to enhance the palmprint and preserve the information, Genetic Algorithm (GA) is used to optimize the value of beta and gamma based on entropy value . The experiments results show that the proposed method does enhancement and brightness preservation and thereby improves information in the palmprint. Entropy of images is used as a fitness function in this work.

A wavelet-based dominant feature extraction algorithm for palm-print recognition

In this paper, a multi-resolution feature extraction algorithm for palm-print recognition is proposed based on two-dimensional discrete wavelet transform (2D-DWT), which efficiently exploits the local spatial variations in a palm-print image. The entire image is segmented into several small spatial modules and the effect of modularization in terms of the entropy content of the palm-print images has been investigated. A palm-print recognition scheme is developed based on extracting dominant wavelet features from each of these local modules. In the selection of the dominant features, a threshold criterion is proposed, which not only drastically reduces the feature dimension but also captures precisely the detail variations within the palm-print image. It is shown that, because of modularization of the palm-print image, the discriminating capabilities of the proposed features are enhanced, which results in a very high within-class compactness and between-class separability of the extracted features. The effect of using different mother wavelets for the purpose of feature extraction has been also investigated. A principal component analysis is performed to further reduce the feature dimension. From our extensive experimentations on different palm-print databases, it is found that the performance of the proposed method in terms of recognition accuracy and computational complexity is superior to that of some of the recent methods.

Texture based Palmprint Recognition using Simple Methods

Now-a-Days Biometric based recognition is the most popular human recognition pattern. Biometric based recognition is an approach using the biological features inherent in each individual. They are processed based on the identical, portable and arduous duplicate characteristics. In the Palmprint recognition application implementing more details apart from principle lines or minutiae will be much helpful. In this paper, proposed a texture based palmprint recognition system. It is suitable to large organizations for maintaining employee entry record. Here, presents an algorithm to extract the features from region of interest (ROI) of palmprint images. In this approach 128 X 128 ROI images of the Hong Kong Polytechnic University 2D_3D_ palmprint database. From these images, extracted some texture features by using of simple methods. Training set is prepared with the help of K no. of samples per user, where K varies from 1to 4. Results are checked against remaining images in image recognition mode. Results are encouraging.

Implementation and evaluation of a remote authentication system using touchless palmprint recognition

When a cellular phone is lost or stolen, it may be used improperly or the personal information may be stolen from it by a malicious user. Biometric authentication such as palmprint recognition is the strongest of the personal authentication technologies designed to prevent such misuse. In biometric authentication, when compared with a local authentication model, a remote authentication model has several advantages such as direct authentication and authentication levels. Ito et al. proposed several palmprint recognition schemes using correspondence matching based on the phase-only correlation. However, these schemes require a palmprint image to be captured with the hand touching the dedicated device, while palmprint images must be captured without such physical contact when using cellular phones. Thus, these schemes cannot be applied to cellular phones since there are large positioning gaps and large differences in brightness and distortion between the images. Furthermore, they have not been implemented in cellular phones and their performances have not been evaluated either. In this paper, we adopt a remote authentication model from the two types of biometric authentication incorporating the above advantages and propose a remote system between a cellular phone and an authentication server. We implement the proposed system using two different types of Android terminal as the terminal on the user side. We also show the validity of the proposed system by examining and confirming the accuracy and processing time. We furthermore discuss the problem of an impersonation attack on the proposed system and consider solutions to this problem from the viewpoints of security and usability. Then, we adopt a palmprint recognition scheme as a biometric authentication scheme and, in particular, use a palmprint recognition algorithm that incorporates Yoruk et al.'s preprocessing technique to Ito et al.'s and Iitsuka et al.'s schemes.

Palmprint Recognition using Feature Level Fusion

Problem statement: Palmprint based biometric method has gained high i mpact over the other biometric methods due to its ease of acquisit ion, reliability and high client acceptance. Multip le feature extraction from image gives higher accuracy of the authentication system. Approach: This study presents the palmprint based identification m ethodology which uses the Gabor wavelet entropy to extract multiple features existing on the palm p rint, by using a feature level fusion using Dempste r- Shafer theory and are classified using nearest neig hbor approach. A feature having the same vector can be grouped together using wavelet transform. A different feature of image using wavelet can be extracted. Some of the features that can be extract ed using wavelet entropy consist of contrast, correlation, energy and homogeneity. The features a re fused at feature levels. Palmprint matching is then performed by using the nearest neighbor classi fier. Results and Conclusion: We selected 100 individuals' left hand palm images; every person is 6 and the total is 600. Later we got every person each palm image as a template (total 100). The rema ining 500 were treated as the training samples. The experimental results achieve recognition accura cy of 98.6% and interesting working point with False Acceptance Rate (FAR) of = 0.03% and False Rejection Rate (FRR) of = 1.4% on the publicly available database of The Hong Kong Polytechnic University. Experimental assessment using palmprint image databases clearly validates the eff icient recognition performance of the suggested algorithm compared with the conventional palmprint recognition algorithms.

Palmprint Image Processing and Linear Discriminant Analysis Method

In this paper, the method of processing and linear discriminant analysis of palmprint image is proposed. The palmprint image processing focuses on the location and segmentation which involves rotation and transition. By means of finding the two locate points about the index finger and middle finger, ring finger and little finger, the palmprint image is rotated and corrected a new coordinate system is created, which determines the region of interest (ROI). Linear discriminant analysis is a method of Gabor plus improved two-dimensional linear discriminant (Gabor+I2DLDA) which is improved 2DLDA method by integrating the Gabor wavelet representation of palm images is proposed. Traditional two-dimensional linear discriminant (2DLDA) method eliminates the column relevance of the image, while improved 2DLDA is a direct 2DLDA method which is defined in the traditional 2DLDA basis. The Gabor wavelets are used to extract palmprint features. The proposed Gabor+I2DLDA yields greater palmprint recognition accuracy while reduces the dimension. The experiment results show that location accuracy and recognition accuracy. The effectiveness of the proposed method is also verified using the PolyU palmprint databases for palmprint recognition.

Block Statistic for Palmprint Recognition Based on High Frequency Coefficients under Wavelet Transform

Palmprint recognition for identification provides a new scheme for information security. This paper presents a block statistic method based on high frequency coefficients under wavelet transform for palmprint identification. Firstly, the method decomposes region of interest (ROI) of the palmprint with the wavelet transform. Then it blocks the high-frequency sub-image. The mean and the variance for each sub-block are found. All the means and the variances constitute feature vector for the image. At last the nearest neighbor classifier is used to classify the images. The method was tested on the basis of UST palmprint image database. From the experimental results, the method can satisfy the uses without excessive demands for collection images.

Automatic Personal Identification Using Feature Similarity Index Matching

Problem statement: Biometrics based personal identification is as an effective method for automatically recognizing, a person’s identity with high confidence. Palmprint is an essential biometric feature for use in access control and forensic applications. In this study, we present a multi feature extraction, based on edge detection scheme, applying Log Gabor filter to enhance image structures and suppress noise. Approach: A novel Feature-Similarity Indexing (FSIM) of image algorithm is used to generate the matching score between the original image in database and the input test image. Feature Similarity (FSIM) index for full reference (image quality assurance) IQA is proposed based on the fact that Human Visual System (HVS) understands an image mainly according to its low-level features. Results and Conclusion: The experimental results achieve recognition accuracy using canny and perwitt FSIM of 97.3227 and 94.718%, respectively, on the publicly available database of Hong Kong Polytechnic University. Totally 500 images of 100 individuals, 4 samples for each palm are randomly selected to train in this research. Then we get every person each palm image as a template (total 100). Experimental evaluation using palmprint image databases clearly demonstrates the efficient recognition performance of the proposed algorithm compared with the conventional palmprint recognition algorithms.

Palmprint Recognition using Line Edge Map

How to make use of palmprint to do the personal identification is our studied focus. Lines feature, among all the features of palmprint, is a very important feature and can be extracted from the low-resolution palmprint images. However, considering the palmprint lines are very irregular and hardly accurate depicted with mathematics, we proposed an approach to approximately model these lines based line edge map, which incorporates spatial and structural information of palmprint lines, and employed the improved line segment Hausdorff Histance to match two sets of line segments. The experiment shows our approach can achieve very effective personal identification.

Multispectral Palmprint Recognition Using a Quaternion Matrix

Palmprints have been widely studied for biometric recognition for many years. Traditionally, a white light source is used for illumination. Recently, multispectral imaging has drawn attention because of its high recognition accuracy. Multispectral palmprint systems can provide more discriminant information under different illuminations in a short time, thus they can achieve better recognition accuracy. Previously, multispectral palmprint images were taken as a kind of multi-modal biometrics, and the fusion scheme on the image level or matching score level was used. However, some spectral information will be lost during image level or matching score level fusion. In this study, we propose a new method for multispectral images based on a quaternion model which could fully utilize the multispectral information. Firstly, multispectral palmprint images captured under red, green, blue and near-infrared (NIR) illuminations were represented by a quaternion matrix, then principal component analysis (PCA) and discrete wavelet transform (DWT) were applied respectively on the matrix to extract palmprint features. After that, Euclidean distance was used to measure the dissimilarity between different features. Finally, the sum of two distances and the nearest neighborhood classifier were employed for recognition decision. Experimental results showed that using the quaternion matrix can achieve a higher recognition rate. Given 3000 test samples from 500 palms, the recognition rate can be as high as 98.83%.

Transform based approaches for Palmprint Identification

In this paper, identification of a person is carried out using Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) coefficients as palmprint features. Dimensionality reduction was carried out using Principal Component Analysis (PCA) and the reduced features were classified using Support Vector Machine (SVM). The segmented palmprint images used in this study are from the IITD palmprint database. Here, we describe the palmprint identification methodology, which can be summarized in a number of distinct steps: (1) preprocessing, (2) feature extraction, and (3) classification. Experiments were developed on a database of 100 images from 20 individuals. This study shows that DCT is better for palmprint identification with an accuracy of 99% compared to DWT which gives 96.66% accuracy.

A Novel 3-D Palmprint Acquisition System

Palmprints have been widely studied for personal authentication because they are highly accurate and incur low costs. Most of the previous work has focused on two-dimensional (2-D) palmprint identification. However, the inner surfaces of palms contain not only texture information but also shape information. Unfortunately, 2-D palmprint systems lose the shape information when capturing palmprint images. Hence, three-dimensional (3-D) information is important for palmprint systems. In this paper, we have designed and developed a novel 3-D palmprint acquisition system based on structured-light imaging technology. The acquisition system can obtain 3-D palmprint information and, at the same time, the corresponding 2-D texture, which are used for personal authentication. A 3-D palmprint database that contains 8000 samples has been established by using the developed acquisition system, and the test results illustrate the effectiveness of our system.

A Fast and Accurate Palmprint Recognition System Based on Minutiae

Palmprint recognition is a challenging problem, mainly due to low quality of the pattern, large nonlinear distortion between different impressions of the same palm and large image size, which makes feature extraction and matching computationally demanding. This paper introduces a high-resolution palmprint recognition system based on minutiae. The proposed system follows the typical sequence of steps used in fingerprint recognition, but each step has been specifically designed and optimized to process large palmprint images with a good tradeoff between accuracy and speed. A sequence of robust feature extraction steps allows to reliably detect minutiae; moreover, the matching algorithm is very efficient and robust to skin distortion, being based on a local matching strategy and an efficient and compact representation of the minutiae. Experimental results show that the proposed system compares very favorably with the state of the art.

Embedded Palmprint Recognition System Using OMAP 3530

We have proposed in this paper an embedded palmprint recognition system using the dual-core OMAP 3530 platform. An improved algorithm based on palm code was proposed first. In this method, a Gabor wavelet is first convolved with the palmprint image to produce a response image, where local binary patterns are then applied to code the relation among the magnitude of wavelet response at the ccentral pixel with that of its neighbors. The method is fully tested using the public PolyU palmprint database. While palm code achieves only about 89% accuracy, over 96% accuracy is achieved by the proposed G-LBP approach. The proposed algorithm was then deployed to the DSP processor of OMAP 3530 and work together with the ARM processor for feature extraction. When complicated algorithms run on the DSP processor, the ARM processor can focus on image capture, user interface and peripheral control. Integrated with an image sensing module and central processing board, the designed device can achieve accurate and real time performance.