Multispectral Palmprint Encoding and Recognition

Palmprints are emerging as a new entity in multi-modal biometrics for human identification and verification. Multispectral palmprint images captured in the visible and infrared spectrum not only contain the wrinkles and ridge structure of a palm, but also the underlying pattern of veins; making them a highly discriminating biometric identifier. In this paper, we propose a feature encoding scheme for robust and highly accurate representation and matching of multispectral palmprints. To facilitate compact storage of the feature, we design a binary hash table structure that allows for efficient matching in large databases. Comprehensive experiments for both identification and verification scenarios are performed on two public datasets -- one captured with a contact-based sensor (PolyU dataset), and the other with a contact-free sensor (CASIA dataset). Recognition results in various experimental setups show that the proposed method consistently outperforms existing state-of-the-art methods. Error rates achieved by our method (0.003% on PolyU and 0.2% on CASIA) are the lowest reported in literature on both dataset and clearly indicate the viability of palmprint as a reliable and promising biometric. All source codes are publicly available.Comment: Preliminary version of this manuscript was published in ICCV 2011. Z. Khan A. Mian and Y. Hu, "Contour Code: Robust and Efficient Multispectral Palmprint Encoding for Human Recognition", International Conference on Computer Vision, 2011. MATLAB Code available: https://sites.google.com/site/zohaibnet/Home/code

An Efficient Vein Pattern-based Recognition System

This paper presents an efficient human recognition system based on vein pattern from the palma dorsa. A new absorption based technique has been proposed to collect good quality images with the help of a low cost camera and light source. The system automatically detects the region of interest from the image and does the necessary preprocessing to extract features. A Euclidean Distance based matching technique has been used for making the decision. It has been tested on a data set of 1750 image samples collected from 341 individuals. The accuracy of the verification system is found to be 99.26% with false rejection rate (FRR) of 0.03%.Comment: IEEE Publication format, International Journal of Computer Science and Information Security, IJCSIS, Vol. 8 No. 1, April 2010, USA. ISSN 1947 5500, http://sites.google.com/site/ijcsis

Palm vein recognition using scale invariant feature transform with RANSAC mismatching removal

Palm vein recognition has been gaining increasing interest as a biometric method, although there still remains an issue regarding difficulties in obtaining robust signals. In this paper, the effects of random sample consensus point mismatching removal and the use of different wavelengths of illumination on the recognition rate are investigated. The CASIA multi-spectral palm print image database was used to provide input signals and the scale invariant feature transform (SIFT) and random sample consensus (RANSAC) mismatching removal approaches were adopted for vein extraction and point feature matching. The results show that the RANSAC mismatching point removal was able to eliminate outliers while preserving the appropriate SIFT key points and that this led to an improvement in the equal error rate metric, signifying better recognition performance. The palm vein recognition system was found to achieve a better verification rate when infrared illumination in a specific spectral band was used to obtain the palm vein image

Evaluation of a Vein Biometric Recognition System on an Ordinary Smartphone

Nowadays, biometrics based on vein patterns as a trait is a promising technique. Vein patterns satisfy universality, distinctiveness, permanence, performance, and protection against circumvention. However, collectability and acceptability are not completely satisfied. These two properties are directly related to acquisition methods. The acquisition of vein images is usually based on the absorption of near-infrared (NIR) light by the hemoglobin inside the veins, which is higher than in the surrounding tissues. Typically, specific devices are designed to improve the quality of the vein images. However, such devices increase collectability costs and reduce acceptability. This paper focuses on using commercial smartphones with ordinary cameras as potential devices to improve collectability and acceptability. In particular, we use smartphone applications (apps), mainly employed for medical purposes, to acquire images with the smartphone camera and improve the contrast of superficial veins, as if using infrared LEDs. A recognition system has been developed that employs the free IRVeinViewer App to acquire images from wrists and dorsal hands and a feature extraction algorithm based on SIFT (scale-invariant feature transform) with adequate pre- and post-processing stages. The recognition performance has been evaluated with a database composed of 1000 vein images associated to five samples from 20 wrists and 20 dorsal hands, acquired at different times of day, from people of different ages and genders, under five different environmental conditions: day outdoor, indoor with natural light, indoor with natural light and dark homogeneous background, indoor with artificial light, and darkness. The variability of the images acquired in different sessions and under different ambient conditions has a large influence on the recognition rates, such that our results are similar to other systems from the literature that employ specific smartphones and additional light sources. Since reported quality assessment algorithms do not help to reject poorly acquired images, we have evaluated a solution at enrollment and matching that acquires several images subsequently, computes their similarity, and accepts only the samples whose similarity is greater than a threshold. This improves the recognition, and it is practical since our implemented system in Android works in real-time and the usability of the acquisition app is high.MCIN/AEI/ 10.13039/50110001103 Grant PDC2021-121589-I00Fondo Europeo de Desarrollo Regional (FEDER) and Consejería de Transformación Económica, Industria, Conocimiento y Universidades de la Junta de Andalucía Grant US-126514

Vein palm recognition model using fusion of features

One of the most promising mechanisms in the field of security and information safety is authentication based on palm vein. The main reasons that vein palm becomes an authentication method is because of its distinctive privacy, as it is difficult to manipulate or change its results, because of the location of the vein within the palm. With the use of this technology, it has become easy to maintain data from unauthorized access and unwanted persons. In this work proposed model are suggested that contain four stages to reach the results: in the first stage is the pre-processing stage where histogram equation was used to enhance the image and the properties are shown, the second stage is the extracting the properties where, Gabor filter and 2-discrete wavelet filters are suggested for features extraction, where it is considered one of the most important filters used to extract the features, as well as in the third stage "PCA" are used for data or features reduction, because of its advantages in analyzing the features and reducing the spacing between them. As for the last stage, the Euclidean distance used to measure the spacing. The results were acceptable and convincing, since the similarity ratio 96.2%. These results were obtained after several tests and using the Gabor filter with 2D-discrete wavelet transform and principal component analysis (PCA), I got the best results

Wrist vascular biometric recognition using a portable contactless system

Human wrist vein biometric recognition is one of the least used vascular biometric modalities. Nevertheless, it has similar usability and is as safe as the two most common vascular variants in the commercial and research worlds: hand palm vein and finger vein modalities. Besides, the wrist vein variant, with wider veins, provides a clearer and better visualization and definition of the unique vein patterns. In this paper, a novel vein wrist non-contact system has been designed, implemented, and tested. For this purpose, a new contactless database has been collected with the software algorithm TGS-CVBR®. The database, called UC3M-CV1, consists of 1200 near-infrared contactless images of 100 different users, collected in two separate sessions, from the wrists of 50 subjects (25 females and 25 males). Environmental light conditions for the different subjects and sessions have been not controlled: different daytimes and different places (outdoor/indoor). The software algorithm created for the recognition task is PIS-CVBR®. The results obtained by combining these three elements, TGS-CVBR®, PIS-CVBR®, and UC3M-CV1 dataset, are compared using two other different wrist contact databases, PUT and UC3M (best value of Equal Error Rate (EER) = 0.08%), taken into account and measured the computing time, demonstrating the viability of obtaining a contactless real-time-processing wrist system.Publicad

Multi-feature Fusion Using SIFT and LEBP for Finger Vein Recognition

In this paper, multi-feature fusion using Scale Invariant Feature Transform (SIFT) and Local Extensive Binary Pattern (LEBP) was proposed to obtain a feature that could resist degradation problems such as scaling, rotation, translation and varying illumination conditions. SIFT feature had a capability to withstand degradation due to changes in the condition of the image scale, rotation and translation. Meanwhile, LEBP feature had resistance to gray level variations with richer and discriminatory local characteristics information. Therefore the fusion technique is used to collect important information from SIFT and LEBP feature.The resulting feature of multi-feature fusion using SIFT and LEBP feature would be processed by Learning Vector Quantization (LVQ) method to determine whether the testing image could be recognized or not. The accuracy value could achieve 97.50%, TPR at 0.9400 and FPR at 0.0128 in optimum condition.  That was a better result than only use SIFT or LEBP feature

The fundamentals of unimodal palmprint authentication based on a biometric system: A review

Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases

Local Descriptor Approach to Wrist Vein Recognition with DVH-LBP Domain Feature Selection Scheme

Local Binary Pattern (LBP) is one of the well-known image recognition descriptors for texture-based images due to its superiority. LBP can represent texture well due to its ability to discriminate and compute efficiency. However, when it is used to describe textures that are barely visible, such as vein images (especially contactless vein), its discrimination ability is reduced, which leads to lower performance. LBP has extensively been implemented for features extraction in recognition system of hand, eye, face, eye, and other images. Nowadays, there are a lot of developments of hand recognition systems as a hand is a part of the body that can be easily used in the recognition process and it is easier to contact the sensor when taking the image (user-friendly). In particular, a hand consists of various parts that can be used, such as palm and fingers. Other parts like dorsal and wrist can also be used as they have unique characteristics, i.e., they are different from each other, and they do not change with ages. Changes in pixel intensity can be derived from skeletal vein images to distinguish individuals in palm vein recognition. In the previous paper, we proposed a method diagonal, vertical, horizontal local binary pattern (DVH-LBP) for implementing the palm vein recognition system successfully. Through this work, we improve our previous procedure and implement the improved method for recognizing wrist. In particular, this study proposes a new and robust directional extraction technique for encoding the functions of the wrist vein in a simple representation of binary numbers. Simulation results show the low equal error rate (ERR) of the proposed technique is 0.012, and the recognition rate is 99.4%