Multispectral Palmprint Images Research Articles

Learning Spectrum-Invariance Representation for Cross-Spectral Palmprint Recognition

Palmprint recognition provides a potential solution for noninvasive personal authentication due to its excellent contactless property and user-security, and it has attracted tremendous research interest in recent years. However, most existing methods focus on intraspectral palmprint recognition, which requires gallery and probe images to be captured under similar illumination, and thus significantly limit its practical applications in open environments with variant illuminations. In this study, we present a spectrum-invariant feature learning method for cross-spectral palmprint recognition to address the problem that gallery and probe samples are captured under different spectra. First, the blockwise direction-based ordinal measure vectors are formed to represent the intrinsic information of palmprint images. Then, a unified feature projection is jointly learned to map two different spectra of palmprint images into a common feature space, in which the different spectral features have enhanced discriminative power by enlarging their variances while the intraclass features learned from different spectral images are similar. The proposed method can be easily extended to seek the unified spectrum-invariant representation of multiple spectral palmprint images, making it feasible to perform palmprint recognition crossing one spectrum to multiple spectra. Experimental results on two multispectral palmprint image databases demonstrate the promising effectiveness of the proposed method on cross-spectral palmprint recognition.

Ensembling Scale Invariant and Multiresolution Gabor Scores for Palm Vein Identification

Biometric recognition based on palm vein trait has the advantages of liveness detection and high level of security. An improved human palm vein identification system based on ensembling the scores computed from scale invariant features and multiresolution adaptive Gabor features is proposed. In the training phase, from the input palm vein images, the interested palm regions are segmented using 3-valley point maximal palm extraction strategy, an improved method that extracts the maximal region of interest (ROI) easily and properly. Extracted ROI is enhanced using contrast limited adaptive histogram equalization method. From the enhanced image, local invariant features are extracted by applying scale invariant feature transform (SIFT). The texture and multiresolution features are extracted by employing adaptive Gabor filter over the enhanced image. These two features, scale invariant and multiresolution Gabor features act as the templates. In the testing phase, for the test images, ROI extraction, image enhancement, and two different feature extractions are performed. Using cosine similarity and match count-based classification, the score, Ss is computed for the SIFT features. Another score, Sg is computed using the normalized Hamming distance measure for the Gabor features. Both these scores are ensembled using the weighted sum rule to produce the final score, SF for identifying the person. Experiments conducted with CASIA multispectral palmprint image database version 1.0 and VERA palm vein database show that, the proposed method achieves equal error rate of 0.026% and 0.0205% respectively. For these databases, recognition rate of 99.73% and 99.89% respectively are obtained which is superior to the state-of-the-art methods in authentication and identification. The proposed work is suitable for applications wherein the authenticated person should not be considered as imposter.

INTELLIGENT sYSTEM FOR HUMAN AUTHENTICATION USING FUSION OF DORSAL HAND, PALM AND FINGER VEINS

Multimodal biometric systems roughly used to achieve extreme recognition accuracy. This paper reports a novel multimodal biometric system employing intelligent technique to authenticate human by fusion of dorsal hand, palm and finger veins pattern. We improved an image analysis technique to separate region of interest (ROI) from dorsal hand, palm and finger veins image. After separating ROI we construct a sequence of preprocessing steps to enhance dorsal hand, palm and finger veins images using Median filter, Wiener filter, Contrast Limited Adaptive Histogram Equalization (CLAHE) and Homomorphic filter to improve vein image. Our intelligent technique is based on the following intelligent algorithms, namely; principal component analysis (PCA) algorithm for feature extraction and k-Nearest Neighbors (K-NN) classifier for matching operation. The database selected was Bosphorus Hand Vein Database, CASIA Multi-Spectral Palmprint Image Database V1.0 (CASIA database) and the Shandong University Machine Learning and Applications - Homologous Multi-modal Traits (SDUMLA-HMT). The accomplished result for the fusion of three biometric traits was Correct Recognition Rate (CRR) is 99.21% with False Reject Rate (FRR) 0.04%.

Performance Evaluation of ANN based Hand Geometry Recognition

The research paper includes development of Application GUI for the ANN Hand Geometry based Recognition System with initial stages of Image Acquisition, Image Pre-processing and Feature Extraction and ANN Recognition using MATLAB. The application is to be tested on database for accuracy and performance and analytical comparisons are to be made on basis of testing. The research presents a method based on moment invariant method and Artificial Neural Network (ANN) which uses a four-step process: separates the hand image from its background, normalizes and digitizes the image, applies statistical features like Length and Width of the Fingers, Diameter of the Palm Perimeter Measurements, maxima and mini points and finally implements recognition and was successful in the verification as ANN was trained for seven neural net layers with 150000 iterations each. Neural network with MLP is highly efficient. The ANN is trained and tested on a total of 150 input palm images from CASIA Multi-Spectral Palmprint Image Database. The two different datasets are created for Left Palm Images and Right Palm Images. The Dataset1 includes 90 left palm images from 15 subjects with 06 images from each subject. The Dataset2 includes 60 right palm images from 10 subjects with 06 images from each subject.

Multispectral Palm Print and Palm Vein Acquisition Platform and Recognition Method Based on Convolutional Neural Network

Abstract Biometrics recognition takes advantage of feature extraction and pattern recognition to analyze the physical and behavioral characteristics of biological individuals to achieve the purpose of individual identification. As a typical biometric technology, palm print and palm vein have the characteristics of high recognition rate, stable features, easy location and good image quality, which have attracted the attention of researchers. This paper designs and develops a multispectral palm print and palm vein acquisition platform, which can quickly acquire palm spectrum and palm vein multispectral images with seven different wavelengths. We propose a multispectral palm print palmar vein recognition framework, and feature-level image fusion is performed after extracting features of palm print palmar vein images at different wavelengths. Through the multispectral palm print palm vein image fusion experiment, a more feasible multispectral palm print and palm vein image fusion scheme is proposed. Based on the results of image fusion, we further propose an improved convolutional neural network (CNN) for model training to achieve identity recognition based on multispectral palm print palm vein images. Finally, the effects of different CNN network structures and learning rates on the recognition results were analyzed and compared experimentally.

Unsupervised Palm Vein Image Segmentation

In this article the new hybrid algorithm for palm vein image segmentation using convolutional neural network and principal curvatures is proposed. After palm vein image preprocessing vein structure is detected using unsupervised learning approach based on W-Net architecture, that ties together into a single autoencoder two fully convolutional neural network architectures, each simi-lar to the U-Net. Then segmentation results are improved using principal cur-vatures technique. Some vein points with highest maximum principal curva-ture values are selected, and the other vein points are found by moving from starting points along the direction of minimum principal curvature. To obtain the final vein image segmentation the result of intersection of the principal curvatures-based and neural network-based segmentations is taken. The evaluation of the proposed unsupervised image segmentation method based on palm vein recognition results using multilobe differential filters is given. Test results using CASIA multi-spectral palmprint image database show the effectiveness of the proposed segmentation approach.

An Effective Palmprint Recognition Approach for Visible and Multispectral Sensor Images.

Among several palmprint feature extraction methods the HOG-based method is attractive and performs well against changes in illumination and shadowing of palmprint images. However, it still lacks the robustness to extract the palmprint features at different rotation angles. To solve this problem, this paper presents a hybrid feature extraction method, named HOG-SGF that combines the histogram of oriented gradients (HOG) with a steerable Gaussian filter (SGF) to develop an effective palmprint recognition approach. The approach starts by processing all palmprint images by David Zhang’s method to segment only the region of interests. Next, we extracted palmprint features based on the hybrid HOG-SGF feature extraction method. Then, an optimized auto-encoder (AE) was utilized to reduce the dimensionality of the extracted features. Finally, a fast and robust regularized extreme learning machine (RELM) was applied for the classification task. In the evaluation phase of the proposed approach, a number of experiments were conducted on three publicly available palmprint databases, namely MS-PolyU of multispectral palmprint images and CASIA and Tongji of contactless palmprint images. Experimentally, the results reveal that the proposed approach outperforms the existing state-of-the-art approaches even when a small number of training samples are used.

Feature extraction in palmprint recognition using spiral of moment skewness and kurtosis algorithm

Because of their high recognition rates, coding-based approaches that use multispectral palmprint images have become one of the most popular palmprint recognition methods. This paper describes a new multispectral palmprint recognition method that aims to further improve the performance of coding-based approaches by focusing on the local binary pattern (LBP) filters and spiral moments features. The final feature map is derived through a staged process of creating a composite of spiral and LBP features by fusing them together and passing the features through the minimum redundancy maximum relevance transformers. Using Hamming distances, the inter- and intra-similarities of the palmprint feature maps are determined. The experimental technique was evaluated using the available data on the IITD, MSPolyU and PolyU PPDB databases. The results indicate that the method achieved high levels of accuracy in the identification and verification modes. Furthermore, this method outperforms the existing advanced techniques.

Palm Vein Recognition System Based on Derived Pattern and Feature Vectors

Biometrics is a technology for recognition under which Palm vein recognition stems. They are of crucial importance in various applications of high sensitivity. This article develops a palm vein recognition model, based on derived pattern and feature vectors. All the palm print images used in this work were obtained from CASIA Multi-Spectral Palmprint Image Database V1.0 (CASIA database). First, a Region of Interest (ROI) was identified and extracted from the palm print images. Next, Histogram Equalization was used to enhance the area of the palm print image in the Region of Interest. The enhanced image obtained was subjected to the Zhang Suen's Thinning Algorithm to extract appropriate features in the palm print images needed for authentication. The features derived based on this vascular pattern thinning algorithm which are then compared and evaluated to carry out ‘matching'. The Pattern Matching itself was done using the Euclidean Distance for subsequent matching. The model was designed using UML, and implemented with C# and MS SQL on Microsoft Visual Studio platform. The developed system was evaluated based on False Acceptance, False Rejection and Equal Error Rate (EER) values obtained from the system. The results of testing and evaluation show that the developed system has achieved high recognition accuracy.

Multi-spectral palmprint recognition based on oriented multiscale log-Gabor filters

Among several palmprint recognition methods proposed recently, coding-based approaches using multi-spectral palmprint images are attractive owing to their high recognition rates. Aiming to further improve the performance of these approaches, this paper presents a novel multi-spectral palmprint recognition approach based on oriented multiscale log-Gabor filters. The proposed method aims to enhance the recognition performances by proposing novel solutions at three stages of the recognition process. Inspired by the bitwise competitive coding, the feature extraction employs a multi-resolution log-Gabor filtering where the final feature map is composed of the winning codes of the lowest filters’ bank response. The matching process employs a bitwise Hamming distance and Kullback–Leibler divergence as novel metrics to enable an efficient capture of the intra- and inter-similarities between palmprint feature maps. Finally, the decision stage is carried pout using a fusion of the scores generated from different spectral bands to reduce overlapping. In addition, a fusion of the feature maps through two proposed novel feature fusion techniques to allow us to eliminate the inherent redundancy of the features of neighboring spectral bands is also proposed. The experimental results obtained using the multi-spectral palmprint database MS-PolyU have shown that the proposed method achieves high accuracy in mono-spectral and multi-spectral recognition performances for both verification and identification modes; and also outperforms the state-of-the-art methods.

An efficient palmprint recognition method based on block dominat orientation code

A practical palmprint recognition system should have high recognition accuracy, fast matching speed, and high-security capabilities. In the past decades, most previous palmprint recognition systems that were designed on the basis of natural light, have achieved more preferable results in terms of recognition accuracy, matching speed, and security. However, they have certain limitations. It is necessary to improve the security capability of the system and assure recognition accuracy. The research proposes a novel hierarchical multispectral palmprint recognition method based on block dominant orientation code (BDOC). As the multispectral palmprint image is captured under red, green, blue, and near-infrared (NIR) illumination sources, more discriminative information which possibly contributes to improved recognition accuracy and security can be acquired. The experimental results showed that the recognition accuracies were all high in the each spectral band. By analysing the recognition results, the different bands are fused using a feature level fusion scheme to improve recognition accuracy. Meanwhile, as the fusion scheme was based on block selection from different bands, the system was secure because the combination of blocks differed between bands, making stronger discrimination possible. Therefore, the proposed method was provided with higher recognition accuracy, and a high-security capability. It offered a novel means of developing on-line multispectral palmprint systems.

Palm vein recognition with local texture patterns

Biometric recognition using the palm vein characteristics is emerging as a touchless and spoof-resistant hand-based means to identify individuals or to verify their identity. One of the open challenges in this field is the creation of fast and modality-dependent feature extractors for recognition. This article investigates features using local texture description methods. The local binary pattern (LBP) operator as well as the local derivative pattern (LDP) operator and the fusion of the two are studied in order to create efficient descriptors for palm vein recognition by systematically adapting their parameters to fit palm vein structures. Results of experiments are reported on the CASIA multi-spectral palm print image database V1.0 (CASIA database). It is found that the local texture patterns proposed in this study can be adapted to the vein description task for biometric recognition and that the LDP operator consistently outperforms the LBP operator in palm vein recognition.

Contactless Palm Vein Recognition Using a Mutual Foreground-Based Local Binary Pattern

Local binary pattern (LBP) is popular for the texture representation owing to its discrimination ability and computational efficiency, but when used to describe the sparse texture in palm vein images, the discrimination ability is diluted, leading to lower performance, especially for contactless palm vein matching. In this paper, an improved mutual foreground LBP method is presented for achieving a better matching performance for contactless palm vein recognition. First, the normalized gradient-based maximal principal curvature algorithm and $k$ -means method are utilized for texture extraction, which can effectively suppress noise and improve accuracy and robustness. Then, an LBP matching strategy was adopted for similarity measurements on the basis of extracted palm veins and their neighborhoods, which include the vast majority of useful distinctive information for identification while eliminating interference by excluding the background. To further improve the LBP performance, the matched pixel ratio was adopted to determine the best matching region (BMR). Finally, the matching score obtained in the process of finding the BMR was fused with results of LBP matching at the score level to further improve the identification performance. A series of rigorous contrast experiments using the palm vein data set in the CASIA multispectral palmprint image database were conducted. The obtained low equal error rate (0.267%) and comparisons with the most state-of-the-art approaches demonstrate that our method is feasible and effective for contactless palm vein recognition.

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.

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.

Multispectral fusion for palmprint recognition

In order to improve the accuracy of palmprint recognition, multispectral imaging has been employed with a fusion strategy to acquire more discriminative information. The PolyU multispectral palmprint images contain four kinds of palmprint images which are respectively captured under red, green, blue and near infrared illuminations and contain much information than the single band. However, there is also redundant information. How to extract the critical information among the multispectral palmprint images and to fuse them is an emerging problem. In this paper we address the problem of selecting bands from the original four bands, and use the extended general color image discriminant (GCID) model to generate three new color components for further improvement of the recognition performance. Experiments using the PolyU multispectral palmprints database show the effectiveness of the proposed models and fusion strategy.

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%.