Multispectral Palmprint Recognition Using Quaternion Principal Component Analysis

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.

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.

With the widespread application of biometrics in identification systems, palmprint recognition technology, as an emerging biometric technology, has received more and more attention in recent years. Palmprint recognition mainly focuses on image acquisition, preprocessing, feature selection and image matching. Feature extraction and matching are usually the most essential processes in palmprint recognition, and most of the research is based on feature selection and image matching, and many researchers use rich knowledge in machine learning and computer vision to solve these problems. In this paper, we propose a deep multi-view representation learning based multi-spectral palmprint fusion method, which uses deep neural networks to extract feature representation of multi-spectral palmprint images for palmprint classification. In this manner, the unique features of different spectral palmprint images can be used to learn a view-invariant representation of each palmprint. By using view-invariant representation, we can get better palmprint recognition performance than single modality. Experiments are performed on PolyU palmprint data set to validate the effectiveness of the proposed method.

The main purpose of this paper is to create a palmprint recognition system (PPRS) that uses the curvelet transform and co-occurrence matrix to recognize a hand's palmprint. The suggested system is composed of several stages: in the first stage, the region of interest (ROI) was taken from a palmprint image, then in the second stage, the curvelet transform was applied to the (ROI) to get a blurred version of the image, and finally, unsharp masking process and sobel filtering were done for edge detection. The third stage involves feature extraction using a co-occurrence matrix to obtain 16 features, while the fourth stage inclusion is the training and testing of the suggested approach. The algorithm ACO (ant colony optimization) has been adopted to evaluate the shortest path to the goal. CASIA PalmprintV dataset of 100 people (60 male and 40 female) was used in proposed work to rate the performance of the proposed system. ARR and EER metrics have been adopted to assess the performance of the proposed system. The experimental results showed a very high recognition rate (ARR) that reaches 100% for the right hand of a male and the left hand of a female. The overall accuracy rate (ARR) reaches 98.5% and EER equals 0.015.

With the increasing demand of highly accurate and robust authentication system, palm print has widely been used in personal identification. Multispectral palm print has been proposed to get more distinguishing feature information and improve resistance to spoof. The vivid texture information of palm print presenting at different resolutions offers abundant prospects in personal recognition. So this paper describes a method to authenticate individuals based on multi-scale and multi-resolution palm print recognition. First, we extract palm print feature based on NSCT (nonsubsampled Contour transform), and store the feature using a hash table. Then the scores generated from each set of palm print image under red, green, blue and NIR spectrum, are combined using score level fusion method. This method use SUM and MAX operators. Comparatively low values of equal error rate and high recognition rate have been obtained for all fusion techniques. Multispectral palm print verification results on the ROI image libraries of PolyU demonstrate the effectiveness and accuracy of the proposed method.

systems. This paper introduces a new blind watermarking technique to address the vulnerabilities of a palmprint recognition system against replay attacks. In particular, we focus on protecting the system against changing or submitting a fake palmprint image at the enrollment and/or recognition stage while still not affecting the recognition performance. To achieve this, the watermark is embedded into the palm's region of intersect which inherently makes it robust to common attacks. The origin of the palmprint images is proven by checking the presence of the watermark in the watermarked palmprint image by comparing the Maximum Likelihood (ML) ratio of a given binary hypotheses to a determined threshold based on the statistical model of the watermarked coefficients. The results obtained have shown that using watermarking concept can efficiently protect a palmprint recognition system against replay attacks.

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...

In this paper, a recently developed local feature descriptor, namely directional local binary patterns (DLBP), was first proposed for palmprint recognition. Compared with local binary patterns (LBP) and directional binary code (DBC), DLBP contains more information on both edge and texture. A cascade structure using AdaBoost algorithm is then used to reduce the feature dimension of DLBP and computational costs of classification. The proposed approach was applied to fuse multispectral palmprint images captured under red, green, blue and near-infrared (NIR) lighting sources for personal identification. Experimental results suggest that the proposed algorithm performs much better than DBC, LBP and PalmCode in identifying palmprint images captured using different illuminations. When fusing the multispectral images, the proposed approach has also been shown to achieve higher accuracy than other methods in literature such as QPCA (Quaternion PCA) and QDWT (Quaternion Discrete Wavelet Transform).

Palm print is a unique and reliable biometric characteristic with high usability. Many palm print recognition algorithms and systems have been successfully developed in the past decades. Most of the previous works use the white light sources for illumination. Recently, it has been attracting much research attention on developing new biometric systems with both high accuracy and high anti-spoof capability. Multispectral palm print imaging and recognition can be a potential solution to such systems because it can acquire more discriminative information for personal identity recognition. One crucial step in developing such systems is how to determine the minimal number of spectral bands and select the most representative bands to build the multispectral imaging system. This paper presents preliminary studies on feature band selection by analyzing hyper spectral palm print data (420nm~1100nm). Our experiments showed that 2 spectral bands at 700nm and 960nm could provide most discriminate information of palm print. This finding could be used as the guidance for designing multispectral palm print systems in the future.

Palmprint is a very unique and distinctive biometric trait because of features such as a person's inimitable principal lines, wrinkles, delta points, and minutiae. These constitute the main reasons why palmprint verification is considered as one of the most reliable personal identification methods. However, a clear majority of the research on palm-prints are concentrated on 2-D palmprint images irrespective of the fact that the human palm is a 3D-surface. While 2-D palmprint recognition has proved to be efficient in terms of verification rate, it has some essential downsides. These restrictions can adversely affect the performance and robustness of the palmprint recognition system. One of the possible solutions to resolve the limitations associated with 2-D palm print authentication systems is (i) to use a 3-D scanning system and to produce high quality 3-D images with depth information; (ii) to map 3-D palm-print images into 2-D images which may support the usage of 3-D images with both biometric palmprint 2-D image databases and 2-D palmprint recognition tools. The bloom of 3-D technologies has made it easier to capture and store 3-D images. The problem of a direct mapping approach is that a large section of the palm is hard-pressed on the scanner surface during 2-D based acquisition. This paper proposes a novel technique to unravel/map 3-D palm images to its equivalent 2-D palm-print image. This image can be then used to perform efficient and accurate 2-D identification/ verification. Experimental results and discussions will also be presented.

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.

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.

Robust palmprint recognition based on the fast variation Vese–Osher model

Palmprint recognition system has been one promising biometric system used in Presence System. There are some methods to recognize the individual palmprint as well as to extract its feature. In this research, two recognition methods are compared, i.e., backpropagation neural network and similarity measure using Euclidean distance. While, for feature extraction, we implemented Principal Components Analysis (PCA) method. From the research, it can be concluded that from test results, the best recognition using backpropogation neural networks is 93.33% which is reached when parameters used are: 100 principal components, 1 hidden layer, and 75 neurons. While, implementation of similarity measure using Euclidean distance, the best recognition rate is 96.67% which is reached when 75 principal components are used. When considering the time consumed in recognition, the Euclidean distance gives the better result, i.e. 17.09 seconds, while using backpropagation neural network with 75 neurons, time consumed is 425 seconds. Therefore, from this research, recognition implementation combining both PCA and Euclidean distance are more suggested rather than using combination of PCA and backpropagation neural network.

Spectroscopic techniques have been researched for intravascular diagnostic imaging of atherosclerotic plaque. Nearinfrared (NIR) light efficiently penetrates of biological tissues, and the NIR region contains the characteristic absorption range of lipid-rich plaques. The objective of this study is to observe atherosclerotic plaque using a NIR multispectral angioscopic imaging. Atherosclerotic plaque phantoms were prepared using a biological tissue model and bovine fat. For the study, we developed an NIR multispectral angioscopic imaging system with a halogen light, mercury-cadmiumtelluride camera, band-pass filters and an image fiber. Apparent spectral absorbance was obtained at three wavelengths, 1150, 1200 and 1300 nm. Multispectral images of the phantom were constructed using the spectral angle mapper algorithm. As a result, the lipid area, which was difficult to observe in a visible image, could be clearly observed in a multispectral image. Our results show that image-enhanced observation and quantification of atherosclerotic plaque by NIR multispectral imaging at wavelengths around 1200 nm is a promising angioscopic technique with the potential to identify lipid-rich plaques.