Query: Low-cost Biometric Recognition System based on NIR Palm Vein Image

Candidate papers:

abstract: Palm vein recognition is motivated by the advantages of high security and liveness detection, but its popularity is prevented by the cost of palm vein capture devices. This study proposes a low cost and practical palm vein recognition system. First, the authors' system captures near infrared (NIR) palm vein image with complementary metal oxide semiconductor camera in lieu of an NIR charge coupled device camera. The goal is to reduce the cost of palm vein capture devices greatly. Second, this study adopts thenar area on the palm as the region of interest (ROI) for further palm vein recognition. The goal is to get the rich vessel and avoid the effect of palmprint. Finally, the discriminate palm vein features are extracted based on Haar wavelet decomposition and partial least squares algorithm on the ROI image. The goal is to increase the recognition accuracy, though the resolution of the image is low. A database with 1500 palm vein images from 250 samples is setup with the capture device. Experiments in the self built database and a public database show the effectiveness of the scheme.
author_names: Wei Wu

Stephen J Elliott

Seng-Fong Lin

Wei-qi Yuan
title: Low cost biometric recognition system based on NIR palm vein image
venue: IET Biom.
year: 2019

abstract: This paper proposes a low cost personal identification system that uses the combined palm vein and palmprint biometric features. The system consists of near infrared and visible light emitting diode (LED) arrays, a low cost visual sensor, a Xilinx chip, and other components. A real time image quality assessment (IQA) method for the combined palm vein and palmprint biometric features is also proposed. Two types of the LED with central frequency spectra of 890 and 680 nm are used to capture the palm vein and palmprint, respectively. The adaptive feedback control of the diode brightness is in accordance with the image quality assessed by the combined 2D entropy and local 2D entropy. The palm vein and palmprint images are acquired nearly simultaneously, and each acquired image undergoes a few preprocessing steps for extraction of the vein and print patterns. We use an image level wavelet based fusion strategy to reduce image storage requirement for the embedded platform and implement a complex wavelet based fusion strategy for the PC platform. A deep scattering convolutional network is applied for extracting the features of the fused images, and a multi class support vector machine is used for training and recognition. Characteristics of some vision based personal identification systems are discussed. The proposed real time IQA method with fusion strategy and feature extraction algorithm in our prototype system has substantially less operational requirements than that of the previous fusion strategies. It also demands less memory and yields lower equal error rate than the classical feature extraction algorithms.
author_names: Peng Chen

Baojin Ding

Haixia Wang

Ronghua Liang

Yilong Zhang

Wei Zhu

Yi-Peng Liu
title: Design of Low Cost Personal Identification System That Uses Combined Palm Vein and Palmprint Biometric Features
venue: IEEE Access
year: 2019

abstract: Biometric authentication systems are a growing field of research that represent an alternative to the more common authentication systems and often offer better performances. Among the available technologies the palm vein recognition represents a non invasive, reliable and affordable solution. Thanks to its appreciated peculiarities, this technology is carving a spot in the market of biometric recognition both at the consumer and business level. In this paper we describe the development of an embedded standalone palm vein authentication system. In order to realize a contact less acquisition system, a solid ROI locating algorithm is proposed. A combination of Gabor filters and histograms calculations is investigated as a method for creating biometric templates. The prototype has been developed using the low cost single board computer Raspberry Pi 3 and it is thought to handle all the steps, from the acquisition of the image of the hand, to the recognition of the user. The entire authentication process is completed in 1.5 2s, with a FAR of 0.32% a FRR of 1.58% and an EER of 1.45% Moreover, thanks to its low cost and to the on site extraction of the biometric template, the proposed device perfectly fits as terminal of a bigger authentication system, with multiple access points and a central system where the matching and storage occur.
author_names: Pierandrea Cancian

Guido Walter Di Donato

Vincenzo Rana

Marco D Santambrogio
title: An embedded Gabor based palm vein recognition system
venue: 2017 IEEE EMBS International Conference on Biomedical Health Informatics (BHI)
year: 2017

abstract: This paper describes the development of a real time system for the recognition of a real human subject using the palm dorsa subcutaneous vein pattern (PDSVP) as a physiological biometric feature. The system has been developed using a low cost, single board computer, called the Raspberry Pi Model B, in conjunction with an infrared sensitive camera, called the Raspberry Pi No Infrared camera, and other components. The camera is sensitive to near infrared (NIR) radiations and this acquisition property has been used to acquire the pattern of vascular structure present in the subcutaneous layer of the dorsum of the human palm. Moreover, an automatic two axis pan tilt mechanism has been developed on which the camera is mounted. This is a completely novel mechanism that has been developed so that the data acquisition is independent of the position where the palm dorsum is positioned, as an automatic palm dorsum self locating strategy is developed using the two axis pan tilt mechanism. Now, the NIR images of the PDSVP acquired, in the aforementioned methodology, do not represent the vein pattern with appreciable clarity and discernibility. Therefore, each image acquired undergoes few steps of image preprocessing, to extract the vein pattern, before they are subjected to testing conditions or they are incorporated into the training database. The recognition strategy has been developed using the collaborative representation based classification. In this paper, we have emphasized upon the most severe case of small sample size, which is single sample per person based training data set creation. The proposed method is tested on a well structured database, of NIR images of the PDSVP, JU NIR V1: NIR Vein Database, developed in the Electrical Instrumentation and Measurement Laboratory, Electrical Engineering Department, Jadavpur University, Kolkata, India. Subsequently, through extensive experimentation it has been proven that the proposed strategy attains substantially high and stable recognition rate. Moreover, the performance of the recognition strategy is highly robust even in the presence of artifacts, such as angular displacement and scaling, that corrupt the NIR images acquired during data acquisition.
author_names: Sandip Joardar

Amitava Chatterjee

Anjan Rakshit
title: A Real Time Palm Dorsa Subcutaneous Vein Pattern Recognition System Using Collaborative Representation Based Classification
venue: IEEE Transactions on Instrumentation and Measurement
year: 2015

abstract: Palm vein based biometric authentication system aims to recognise individuals from their unique palm vein structure which is next to impossible to duplicate owing to the fact that palm veins are present in the subsurface of the skin and not apparent under visual light. The aim of the proposed work is to develop a low cost but efficient system for acquiring images of the veins, processing these images and matching using various algorithms. Images have been acquired using a web camera and an infrared LED illumination that highlights the veins. The region of interest ROI is extracted from the images and then processed. Three techniques for matching are proposed. Principal component analysis PCA, 2D wavelet based feature and template designed exclusively for palm vein ROI is applied over ROI for matching. The accuracy of the each algorithm is deduced to compare the three algorithms. The highest accuracy achieved is 93.54% using template matching technique.
author_names: Gunjan Shah

Sagar Shirke

Sonam Sawant

Yogesh H Dandawate
title: Palm vein pattern based biometric recognition system
venue: Int. J. Comput. Appl. Technol.
year: 2015

abstract: Currently, Biometrics has been utilized the top five modality of face, voice, IRIs, fingerprint, and palm to identify individuals. Comparatively, these Biometrics systems need complex computation to be slow and an easy target to hack. Alternatively, this work proposes a novel biometrics system of highly secured recognition with low computation time using specifically designed biometrics sensor. Consequently, finger vein recognition has been developed. Although, this recognition requires high point of safety measures comes with its individual experiments. The most prominent one being the vein pattern is very difficult to extract because finger vein images are constantly low in quality, seriously hampering the feature extraction and classification stages. Sophisticated algorithms need to be designed with the conventional hardware for capturing finger vein images is modified by using red Surface Mounted Diode (SMD) leds. For capturing images, Canon 750D camera is used with micro lens. The integrated micro lens gives better quality images, and with some adjustments it can also capture finger print. Results have been comparatively improvement for SDUMLA HMT database and extensively evaluated with k nearest neighbors (KNN) algorithm. The (KNN) algorithm is a simple, easy to implement supervised machine learning algorithm that can be used to solve both classification and regression problems. KNN calculations are highly accurate in test data. Using stratified 6 fold analysis on all fingers of all hands in collected database, a maximum accuracy of 100% was achieved with an EER of 0% when select right hand and middle finger, based on the analysis of the 106 persons present in the data set. Many approaches have been used to optimize vein image quality. The proposed system has optimum results as compared to existing related works. The work novelty is due to the hardware design of the sensor within the finger vein recognition system to obtain, simultaneously, finger vein and finger print at low cost, unlimited users for one device and open source.
author_names: Khamis A Zidan

Shereen S Jumaa
title: A highly verified biometric recognition system using an ultra speed specifically developed finger vein sensor
venue: Periodicals of Engineering and Natural Sciences (PEN)
year: 2019

abstract: Currently, Biometrics has been utilized the top five modality of face, voice, IRIs, fingerprint, and palm to identify individuals. Comparatively, these Biometrics systems need complex computation to be slow and an easy target to hack. Alternatively, this work proposes a novel biometrics system of highly secured recognition with low computation time using specifically designed biometrics sensor. Consequently, finger vein recognition has been developed. Although, this recognition requires high point of safety measures comes with its individual experiments. The most prominent one being the vein pattern is very difficult to extract because finger vein images are constantly low in quality, seriously hampering the feature extraction and classification stages. Sophisticated algorithms need to be designed with the conventional hardware for capturing finger vein images is modified by using red Surface Mounted Diode (SMD) leds. For capturing images, Canon 750D camera is used with micro lens. The integrated micro lens gives better quality images, and with some adjustments it can also capture finger print. Results have been comparatively improvement for SDUMLA HMT database and extensively evaluated with k nearest neighbors (KNN) algorithm. The (KNN) algorithm is a simple, easy to implement supervised machine learning algorithm that can be used to solve both classification and regression problems. KNN calculations are highly accurate in test data. Using stratified 6 fold analysis on all fingers of all hands in collected database, a maximum accuracy of 100% was achieved with an EER of 0% when select right hand and middle finger, based on the analysis of the 106 persons present in the data set. Many approaches have been used to optimize vein image quality. The proposed system has optimum results as compared to existing related works. The work novelty is due to the hardware design of the sensor within the finger vein recognition system to obtain, simultaneously, finger vein and finger print at low cost, unlimited users for one device and open source.
author_names: Khamis A Zidan

Shereen S Jumaa
title: A Highly Verified Biometric Recognition System using An Ultra speed specifically developed Finger Vein Sensor
year: 2019

abstract: Biometrics has emerged as a powerful technology for person authentication in various scenarios including forensic and civilian applications. Deployment of biometric solutions that use cues from multiple modalities enhances the reliability and robustness of authentication necessary to meet the increasingly stringent security requirements. However, there are two drawbacks typically associated with multimodal biometrics. Firstly, the image acquisition process in such systems is not very user friendly, primarily due to the time and effort required to capture biometric samples belonging to multiple modalities. Secondly, the overall cost is higher as they employ multiple biometric sensors. To overcome these drawbacks, we employ a single NIR sensor based image acquisition in the proposed approach for hand vein recognition. From the input hand image, a palm vein and four finger vein subimages are extracted. These images are then enhanced by CLAHE and transformed into illumination invariant representation using center symmetric local binary pattern (CS LBP) Further, a hierarchical non rigid matching technique inspired by the architecture of deep convolutional networks is employed for matching the CS LBP features. Finally, weighted sum rule based matching score level fusion is performed to combine the palm vein and the four finger vein modalities. A set of rigorous experiments has been performed on an in house database collected from the left and right hands of 185 subjects and the publicly available CASIA dataset. The proposed approach achieves equal error rates of 0.13% and 1.21% and rank 1 identification rates of 100% and 100% on the in house and CASIA datasets, respectively. Additionally, we compare the proposed approach with the state of the art techniques proposed for vascular biometric recognition in the literature. The important findings are (1) the proposed approach outperforms all the existing techniques considered in this study, (2) the fusion of palm vein and finger vein modalities consistently leads to better performance for all the feature extraction techniques considered in this work. (3) Furthermore, our experimental results also suggest that considering the constituent palm vein and finger vein images instead of the entire hand vein images achieves better performance.
author_names: Shruti Bhilare

Gaurav Jaswal

Vivek Kanhangad

Aditya Nigam
title: Single sensor hand vein multimodal biometric recognition using multiscale deep pyramidal approach
venue: Machine Vision and Applications
year: 2018

abstract: Biometric identification systems based on the recognition of the hands vein pattern are better than the other systems such as fingerprint identification, face, iris, retina, and hand geometry features for using not visible images to the eye, having high acceptability to users and do not require physical contact. This paper presents an identification system based on the dorsal hand vein pattern recognition, using a low cost camera to capture images with near infrared (NIR, curve let transforms for feature detection of images and random forest classification method. The proposed method was tested on a database of 1240 images captured by the authors and the system results presented classification about recognition with low Equal Error Rate (EER)
author_names: Ricardo Janes

Augusto Ferreira Branda Junior
title: A Low Cost System for Dorsal Hand Vein Patterns Recognition Using Curvelets
venue: ICONS 2014
year: 2014

abstract: Security and the authentication of individuals is necessary for many different areas of our lives, with most people having to authenticate their identity on a daily basis; examples include ATMs, secure access to buildings, and international travel. Biometric identification provides a valid alternative to traditional authentication mechanisms such as ID cards and passwords, whilst overcoming many of the deficits of these methods; it is possible to identify an individual based on who they are rather than what they possess or what they remember. Iris/Fingerprint/palm vein/Face recognition are some of the biometric system that can be used to reliably identify a person by analysing the patterns. The objective of the proposed system is to improve the safety of biometric recognition mechanism, by adding liveness assessment in a fast, user friendly, low cost, non disturbing manner and performance through the use of image quality assessment. Thus we propose an secure authentication by combining the above four biometric systems to identify the fake real users. Also we shall propose the best biometric system by comparing Iris/fingerprint/palm vein/face recognition techniques. Also focus on researching the system in accordance to the performance analyzing accuracy and time factors.
author_names: G Merlin Sheeba

G Sivakami

Savithri Monica J
title: Multimodal Biometric Recognition Security System
year: 2015