Palmprint Images Research Articles

STUDY OF PERSON IDENTIFICATION USING PALMPRINT RECOGNITION SYSTEM BASED ON MINUTIAE CYLINDRICAL CODE

In today’s world, biometrics system is used everywhere for the security and personal identification. Palmprint is a very important type of biometrics and it is used mostly in forensic applications. In this paper, high resolution palmprint images are used, feature extraction and matching steps are developed such as system is simple, more accurate and faster. Minutiae Cylindrical code is used for matching purpose. Palmprint recognition is difficult task because of size of large images. For high security applications high resolution palmprints are needed from which more extra information can be extracted. This Palmprint recognition system has many steps to common with fingerprint having optimized and specified steps to process large sized palmprint images. Feature extraction method is used to detect minutiae followed by local matching algorithm.

Fingerprint and Palmprint Recognition using Neighborhood Operation and FAST Features

In this paper presents biometrics based fingerprint and palmprint recognition and authentication system. Fingerprint and palmprint images are enhanced using preprocessing techniques such as morphological operations. The feature extraction techniques such as neighborhood operation and FAST feature algorithm is used to independently extract fingerprint and palmprint features. These techniques are more reliable and faster than traditional techniques used. Experimental results shown recognition rate 89.29% for fingerprint and 100% for palmprint this implies that the proposed methodology has better performance and is more reliable over the techniques proposed and used earlier. Abbrevations SF Single Flat SFF Single Flat Flexi LT Left Thumb finger LI Left Index finger LM Left Middle finger LR Left Ring finger LL Left Little finger

A SIFT-based contactless palmprint verification approach using iterative RANSAC and local palmprint descriptors

Palmprint recognition is a relatively new and effective biometric technology. Most of the traditional palmprint recognition methods are based on contact acquisition devices, which affects their user-friendliness and limits their applications. To overcome these shortcomings, this work proposes a contactless palmprint verification approach based on SIFT, which is composed of three steps, namely, image preprocessing, SIFT feature extraction and matching, and matching refinement. Palmprint images are firstly preprocessed using an isotropic filter, and then the SIFT points are detected and matched. Finally, the matched points are refined by employing a two-stage strategy. In the first stage, an iterative RANSAC (I-RANSAC) algorithm is employed to remove the mis-matched points which fail to satisfy the topological relations. In the second stage, local palmprint descriptors (LPDs) are extracted for SIFT points to further remove the mis-matched points which cannot be distinguished by original SIFT descriptors. The number of final matched SIFT points is taken as the score for decision. Experimental results show that the proposed approach is effective in contactless palmprint recognition, especially when non-linear deformations exist in palmprint images.

Application of an Improved Algorithm for Denoising of Wavelet Threshold in Palmprint Recognition

In the acquisition process of palmprint image, the image is often subject to interference from the outside noise.This noise will affect the future of Palmprint feature extraction. Denoising of wavelet is a common method for denoising of palmprint image. The defect of traditional soft and hard threshold functions in image denoising is analyzed. An improved threshold function is proposed on the basis of soft and hard threshold functions. The conventional hard, soft threshold functions and the improved function are used respectively in image denosing. Experiments prove that the improved wavelet threshold function is better than conventional soft, hard threshold function in denoising effect.

Multispectral palmprint recognition using multiclass projection extreme learning machine and digital shearlet transform

A novel multispectral palmprint recognition method is proposed based on multiclass projection extreme learning machine (MPELM) and digital shearlet transform. Extreme learning machine (ELM) is a novel and efficient learning machine based on the generalized single-hidden-layer feedforward networks, which performs well in classification applications. Many researchers' experimental results have shown the superiority of ELM with classical algorithm: support vector machine (SVM). To further improve the performance of multispectral palmprint recognition method, we propose a novel method based on MPELM in this paper. Firstly, all palmprint images are preprocessed by David Zhang's method. Then, we use image fusion method based on fast digital shearlet transform to fuse the multispectral palmprint images. At last, we use the proposed MPELM classifier to determine the final multispectral palmprint classification. The experimental results demonstrate the superiority of multispectral fusion to each single spectrum, and the proposed MPELM-based method outperforms the SVM-based and ELM-based methods. The proposed method is also suitable for other biometric applications and gets to be work well.

Android Based Palmprint Recognition System

This paper aims to build a palmprint recognition system based on Android. Line detection and non-overlapping blocks methods used on the design of palmprint recognition system. Normalized Euclidean distance was used to match the two feature vector. The similarity degrees was measured by the smallest value. The method was applied to the system can result an accuracy rate about 91,32% (FMR = 5,2%, FNMR = 3,68%, T = 0,265). System testing was done as much as 2500 times to the 50 participants with 3 reference images of each user. The factor that influenced on system accuracy was the process of image acquisition. Palmprint images was taken directly by using camera on the android smartphone device.

Infrared Capture of Palm-Vein Blood Vessel Patterns for Human Authentication

Biometric Technology is fast taking over from the conventional use of ID Cards, PINs, Tokens and Passwords. This is becoming essential because of the issues of forgotten passwords, stolen ID Cards, compromised tokens and spoofed PINs. Palm-print authentication for access control has been deployed in so many areas of security concern in developing countries. Even though it is still in the theoretical stage in Nigeria, this paper seeks to evaluate the stages involved in the processing of palm-print images for identification or human authentication. It focuses on the feature extraction stage which is where the required features are separated from the others. T

Palmprint Authentication System Based on Local and Global Feature Fusion Using DOST

Palmprint is the region between wrist and fingers. In this paper, a palmprint personal identification system is proposed based on the local and global information fusion. The local and global information is critical for the image observation based on the results of the relationship between physical stimuli and perceptions. The local features of the enhanced palmprint are extracted using discrete orthonormal Stockwell transform. The global feature is obtained by reducing the scale of discrete orthonormal Stockwell transform to infinity. The local and global matching distances of the two palmprint images are fused to get the final matching distance of the proposed scheme. The results show that the fusion of local and global features outperforms the existing works on the available three datasets.

Palm Print Edge Extraction Using Fractional Differential Algorithm

Algorithm based on fractional difference was used for the edge extraction of thenar palm print image. Based on fractional order difference function which was deduced from classical fractional differential G-L definition, three filter templates were constructed to extract thenar palm print edge. The experiment results showed that this algorithm can reduce noise and detect rich edge details and has higher SNR than traditional methods.

Research on Applied Technology of Palmprint Recognition Based on Fisher Linear Discriminant and Improved PCA Algorithm

The information applied technology of palmprint recognition is a biometric technology, it’s based on the effective information on the palm (such as: palmprint) to identifies people. The palmprint is unique and characteristic, these are the identification of critical conditions. The feature extraction of palmprint image is a prerequisite for recognition, feature extraction algorithm depends on the quality of the recognition rate and efficiency. This paper presents a method of palmprint recognition algorithm based on Fisher linear discriminant analysis and improved PCA algorithm. The experimental results show that, the recognition rate is improved.

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).

Efficient local representations for three-dimensional palmprint recognition

Palmprints have been broadly used for personal authentication because they are highly accurate and incur low cost. Most previous works have focused on two-dimensional (2-D) palmprint recognition in the past decade. Unfortunately, 2-D palmprint recognition systems lose the shape information when capturing palmprint images. Moreover, such 2-D palmprint images can be easily forged or affected by noise. Hence, three-dimensional (3-D) palmprint recognition has been regarded as a promising way to further improve the performance of palmprint recognition systems. We have developed a simple, but efficient method for 3-D palmprint recognition by using local features. We first utilize shape index representation to describe the geometry of local regions in 3-D palmprint data. Then, we extract local binary pattern and Gabor wavelet features from the shape index image. The two types of complementary features are finally fused at a score level for further improvements. The experimental results on the Hong Kong Polytechnic 3-D palmprint database, which contains 8000 samples from 400 palms, illustrate the effectiveness of the proposed method.

Fingerprint and Palmprint Image Fusion System Based on C#

Fingerprint and Palmprint Image Fusion System Based on C#

Implementation of Palm Print Biometric Identification System Using Ordinal Measures

Personal identification is one of the most important requirements in all e-commerce and criminal detection applications. In this framework, a novel palm print representation method, namely orthogonal line ordinal features, is proposed. The palm print registration, feature extraction, palm print verification and palm print recognition modules are designed to manage the palm prints and the palm print database module is designed to store their palm prints and the person details in the database. The feature extraction module is proposed to extract the ordinal measurements for the palm prints. The verification module is designed to verify the palm print with the personal identification record. The recognition module is proposed to find out the relevant person associated with the palm print image. The proposed palm print recognition scheme uses the intensity and brightness to measure the ordinal measurement. The ordinal measures are estimated for the 4 x 4 regions of the palm print images.

Thenar Palmprint Image Denoising Based on Intermediary Filter

In this paper, based on the value measure of the Intermediary truth value, the Intermediary filtering algorithm is applied to the thenar palmprint image preprocessing. By using the Objective indicators of peak signal to noise ratio (PSNR), it can be seen that compared with the classical de-noising algorithm, the intermediary filtering algorithm is more effective and practicable.

Palmprint Authentication Based on DCT-Based Watermarking

With the rapid development of biometric technology, the security issues have drawn people's greater attention because some biometric features are Easy to forge and are not replaceable. As a result, the security issue of biometric system extremely critical to be solved. In this paper, we introduce a public watermarking algorithm which is based on Discrete Cosine Transformation (DCT). By using the proposed algorithm, the finger geometry is embedded into the region of interest (ROI) of palmprint image. This method has the ability to increase the security of both the palmprint image and the finger geometry. Experimental results performed on a database of 86 hands (10 impressions per hand) show that the proposed algorithm is effective, and the watermarking operation has little impact on the recognition performance.

Image feature extraction based on modified fast sparse coding algorithm

On the basis of the Fast Sparse Coding(FSC) model,considering the maximum sparse distribution of feature coefficients and the orthogonality of feature bases of an image,a Modified FSC(MFSC) model was proposed in this paper.This FSC algorithm was based on iteratively solving two convex optimization problems: L1-norm based regularized least square problem and L2-norm based constrained least square problem,and it can realize the learning of complete bases and overcomplete bases,as well as efficiently extract the features of images.Moreover,the convergence speed of FSC is quicker than that of Basic Sparse Coding(BSC).The images of natural scene and palmprint were used to test the property of FSC algorithm proposed by the authors in feature extraction,and then the extracted features were utilized to image reconstruction.Compared with reconstructed images obtained by BSC,the experimental results verify the validity of the modified FSC in quickly extracting image features.

Do multispectral palmprint images be reliable for person identification?

This paper is concerned with an investigation of multispectral palmprint images for improving person identification by replying to the question: can multispectral palmprint images be reliable for s...

Research and Evaluation of Multispectral Feature Selection and Fusion Model for Palmprint Recognition

Palmprint is widely used in personal identification for an accurate and robust recognition. Multispectral palmprint images capture under different illumination, including Red, Green, Blue and Infrared maybe contribute to the recognition results. However, the evaluation of selection and fusion of how this different spectral images can contribute to improve the robustness of the recognition system is imperative. In this paper, a novel wavelet-based multispectral fusion strategy is presented firstly to obtain the fused images; then block singular value decomposition (B-SVD) is applied for feature extraction; Finally back propagation (BP) neural network method is adopted for authentication. The proposed algorithm is evaluated on PolyU database which contains palmprint images from 500 individuals from four independent frequent band. The obtained results show robustness of our multispectral palmprint image fusion and selection model in comparison with the single spectral palmprint image that presented in the literature.

BI-FEATURE VERIFICATION FOR PALMPRINT IMAGES CAPTURED IN PEGLESS SCENARIOS

This paper presents a reliable and robust palmprint verification approach that involves using a bi-feature, biometric, palmprint feature-point number (FPN) and a histogram of oriented gradient (HOG). The bi-feature was fused and verified using a support vector machine (SVM) at the feature level. The approach has the advantages of capturing palm images in pegless scenarios with a low cost and low-resolution (100 dpi) digital scanner, and one sensor can capture palmprint bi-feature information. The low-resolution images result in a smaller database. Nine thousand palmprint images were collected from 300 people to verify the validity of the proposed approach. The results showed an accurate classification rate of 99.04%. The experimental results demonstrated that the proposed approach is feasible and effective in palmprint verification. Our findings will help extend palmprint verification technology to security access control systems.