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Personal authentication is getting harder and harder in the internet of things (IoT). Existing methods used for personal authentication, such as passwords and the two-factor authentication (2FA), are inadequate and ineffective due to human error and other attacks. To support more secure IoT, this paper proposes a finger vein based personal authentication method by exploring competitive orientations and magnitudes from finger vein images. Finger vein recognition has been proven to be a reliable and promising solution for biometric-based personal authentication. The stable and rich piecewise line features in finger vein images can be used to clearly represent finger vein patterns for personal authentication. In this paper, we propose an efficient local descriptor for finger vein feature extraction, namely the histogram of competitive orientations and magnitudes (HCOM). For a finger vein image, two types of local histograms are extracted and fused together to efficiently and adequately represent the competitive information: the histogram of competitive orientations (HCO) and the local binary pattern histogram generated from the image of competitive magnitudes (named as HCMLBP). The extensive experimental results from the application of the proposed method to the public finger vein database MMCBNU_6000, demonstrate that the proposed method outperforms state-of-the-art orientation coding (OC)-based methods and other commonly used local descriptors. Additionally, the proposed method can be used for finger vein image enhancement.

The accurate location of eyes in a facial image is important to many human facial recognition-related applications, and has attracted considerable research interest in computer vision. However, most prevalent methods are based on the frontal pose of the face, where applying them to non-frontal poses can yield erroneous results.In this paper, we propose an eye detection method that can locate the eyes in facial images captured at various head poses. Our proposed method consists of two stages: eye candidate detection and eye candidate verification. In eye candidate detection, eye candidates are obtained by using multi-scale iris shape features and integral image. The size of the iris in face images varies as the head pose changes, and the proposed multi-scale iris shape feature method can detect the eyes in such cases. Since it utilizes the integral image, its computational cost is relatively low. The extracted eye candidates are then verified in the eye candidate verification stage using a support vector machine (SVM) based on the feature-level fusion of a histogram of oriented gradients (HOG) and cell mean intensity features.We tested the performance of the proposed method using the Chinese Academy of Sciences' Pose, Expression, Accessories, and Lighting (CAS-PEAL) database and the Pointing'04 database. The results confirmed the superiority of our method over the conventional Haar-like detector and two hybrid eye detectors under relatively extreme head pose variations. We propose an eye detection method in facial images captured at various head poses.Our method utilizes an integral image, and hence incurs low computational cost.We successfully tested our method on the CAS-PEAL and the Pointing'04 databases.

This paper proposes a novel authentication system using hidden biometric trait called finger vein (FV). Proposed finger vein acquisition device captures the superficial vein pattern present under the skin of finger. The vein pattern of all fingers of human being is not same. Each finger of same person has different vein pattern. It is the hidden part which is not seen by normal eye sight hence less possible to forge. The paper highlight the primary set up of NIR based finger vein acquisition device to capture the superficial vein pattern that shows the unique features require for recognition. This Paper explores the bilateral and contrast limited histogram equalization technique to enhance finger vein raw samples captured by proposed finger vein acquisition . The performance is verified using Local Binary Pattern (LBP) algorithm. The recognition accuracy is represented by graph between True Acceptance Rate (TAR) vs. True Rejection Rate (TRR). It is observed that TAR Vs TRR for standard data base is 94. 5% and for own data base is 94.34%. It shows that FV has potential for personal identification.

In consideration of an increasing demand to adopt the biometric technology to minimize potential security risks, the paper presents a consumer-level biometric system for automatic physical access control. Through development of a near infrared imaging device for biometric acquisition of dorsal hand vein patterns and computation efficient image processing methods, the proposed system is shown to possess a range of desirable operation characteristics for real-world use, which include reliable in identification, convenient to use, high in security, fast in response, low in cost, and simple for installation.

The vein structure is stable over time and can be manipulated for identifying human. The sclera portion of the human eye has blood vessel pattern which is unique for each human being. So, the sclera vein pattern can be used for a useful biometric feature. A few research works has been done over finger vein pattern recognition. Finger vein is an important biometric technique for personal identification and authentication. The finger vein is a blood vessel network under the finger skin. The network pattern is distinct for each individual, unaffected by aging and it is internal i.e. inside human skin which can always guarantee more security authentication. Sclera vein pattern recognition can face a few challenges like: the vein structure moves as the eye moves, low image quality, multilayered structure of the sclera vein and thickness of the sclera vein changes with the excitement level of the human body. To overcome this limitation, the multimodel biometrics is proposed through which the user can be authenticated either sclera vein or finger vein recognition. Sclera vein recognition used Y-shape descriptor and finger vein recognition used repeated line tracking based feature extraction method to effectively eliminate the most unlikely matches respectively. According to the available work in literatures and commercial utilization experiences, sclera vein and finger vein multimodality ensures higher performance and spoofing resistance. Thus building the multimodel biometric system increases the population coverage and improves the accuracy of the human recognition.

Method of human biometric identification by iris of the eye is considered as one of the most accurate and reliable methods of identification. Aim of the research is to solve the problem of edge detection of digital image of the human eye iris to be able to implement human biometric identification system by means of mobile device. To achieve this aim the algorithm of edge detection by Canny is considered in work. It consists of the following steps: smoothing, finding gradients, non-maximum suppression, double thresholding with hysteresis. The software implementation of the Canny algorithm is carried out for the Android mobile platform with the use of high level programming language Java.

Attacks on authentication services are major security concerns. Password-based authentication systems can be compromised using known techniques, such as brute force and dictionary-based attacks. Biometric-based authentication systems are becoming the preferred choice to replace password-based authentication systems. Among several variations of biometrics (e.g., face, eye, fingerprint), iris-based authentication is commonly used in various applications. In iris-based authentication systems, iris images from legitimate users are captured and certain features are extracted to be used for matching during the authentication process. Literature works suggest that iris-based authentication systems can be subject to presentation attacks where an attacker obtains printed copy of the victim's eye image and displays it in front of an authentication system to gain unauthorized access. Such attacks can be performed by displaying static eye images on mobile devices or ipads (known as screen attacks). Since human iris features so not changed, once the iris image is compromised, it is hard to avoid this type of attack. To address this challenge, this paper proposes a framework for iris code generation by considering the changes of the area between the pupil and the sclera due to light density level. The proposed approach relies on capturing iris images using near infrared light. We train HaarCascade and LBP classifiers to capture the area between the pupil and the cornea. The image of iris is then stored in the database. This approach also generates a QR code from the iris. The code acts as a password and the user is required to provide it during authentication. A prototype is built using OpenCV platform tool. The prototype has been tested using samples obtained from publicly available iris database. The initial results show that the proposed approach has lower false positive and false negative rates.

Forearm vein recognition is one of the methods used for identification. In this paper forearms vein features are used for recognition in biometric systems. Forearm vein can be considered more secured compared to other biometric traits because the veins are inside the human body. The forearm recognition has been developed using characteristic points based technique where forearm image is normalized, binarized and thinned, is then a crossing number is used to extract properties of the veins e.g. bifurcation.

Finger vein patterns are unique biometric features, which differentiate from individual to individual, so they are suitable for authentication applications. Systems based on the use of this feature have numerous advantages such as low cost and high accuracy. A new finger vein authentication approach is proposed, which is based on the efficient detection of the non-vein regions, in order to define the main vein patterns. The proposed method is robust in extracting and depicting not only the finger's vein pattern, but also other important features such as the veins' width. The authentication algorithm has been evaluated on a finger vein database of 400 images. The false acceptance and false rejection rates achieved are 0% and 0.5% respectively.

The quest of providing more secure identification system has led to rise in developing biometric systems. Biometrics such as face, fingerprint and iris have been developed extensively for human identification purpose and also to provide authentic input to many security systems in the past few decades. Dorsal hand vein pattern is an emerging biometric which is unique to every individual. In this paper Linear Hough transform is used to extract the features of query and data base images. K-Nearest neighbor Search is used to obtain best match between query image and database. The extraction of the vein patterns was obtained by morphological techniques. Noise reduction filters are used to enhance the vein patterns.

Vein patterns are one of the safest biometric information due to their strong resilience against the impostor attacks. In the presented work, a new approach for biometric authentication using infrared thermal hand vein patterns is proposed. The proposed work presents a Euclidean distance based vein's pattern based biometric authentication that can be used for ascertaining the biometric identity of person under scanner. The vein patterns are grabbed using the infra red (IR) thermal cameras and after applying some image pre-processing operations, a binary image is obtained consisting of veins crossings and intersections. The binary image is thinned using the morphological operations and a single line thinned image pattern is obtained. The thinned image pattern is now examined for intersections extractions and inter-distance between intersections. The inter-distance between intersections of vein patterns are stored in a data base. Further, when a test vein pattern is brought under test, the data base information is compared to that of the test pattern using Euclidean distances. Minimum the Euclidean distance, more is the equivalency of the test pattern to data base pattern

Hand vein patterns are among the biometric traits being investigated today for identification purposes, attracting interest from both the research community and industry. A reliable and robust personal verification approach using dorsal hand vein patterns is presented in this paper. This approach needs less computational and memory requirements and has a higher recognition accuracy than similar methods. In our work, a near-infrared charge-coupled device camera is adopted as an input device for capturing dorsal hand vein images, due to its advantages of the low-cost and non-contact imaging. In the proposed approach, two finger-peaks are automatically selected to define the region of interest in the dorsal hand vein images. In order to obtain effective pattern of dorsal hand vein vascular, we proposed an innovative and robust adaptive Gabor filter method to extract the dorsal hand vein patterns and encode the vein features in bit string representation. The bit string representation, called VeinCode,...

This paper presents an objective evaluation of the effects of eye tracking specification and stimulus presentation on the biometric viability of complex eye movement patterns. Six spatial accuracy tiers (0.5°, 1.0°, 1.5°, 2.0°, 2.5°, 3.0°), six temporal resolution tiers (1000, 500, 250, 120, 75, 30 Hz), and five stimulus types (simple, complex, cognitive, textual, random) are evaluated to identify acceptable conditions under which to collect eye movement data. The results suggest the use of eye tracking equipment capable of at least 0.5° spatial accuracy and 250 Hz temporal resolution for biometric purposes, whereas stimulus had little effect on the biometric viability of eye movements.

In this chapter we present a survey of biometric methods that exploit eye tracking technology for identification and verification purposes. Thanks to the availability of cheap and portable devices, it is now possible to deploy an eye tracker in several settings, both in indoor and outdoor environments. Unlike traditional techniques that prevent unauthorized access to secured systems and places, eye-based approaches have the advantage of allowing a contactless interaction that can sometimes even occur covertly. Different eye features will be considered, such as fixation data, scanpath characteristics, saccade dynamics, pupil size, and oculomotor traits. Moreover, although not strictly biometric techniques, ATM-like approaches will be also presented, where PINs or passwords are entered using the gaze instead of an ordinary keyboard.

We propose a new concept of walkthrough-style finger vein authentication. Adaptive light-sources control enables multi-finger vein capturing in real time. Each finger vein region is precisely detected by utilizing a depth camera. Experiments in a prototype show real-time authentication with high accuracy.

The main aim of this paper is to provide multilevel authentication in biometric systems. Multimodal biometric is the usage of multiple biometric indicators by personal identification systems for identifying the individuals. Multimodal authentication provides more level of authentication than unimodal biometrics which uses only one biometric data such as fingerprint or face or palm print or iris. In this paper, we are using fingerprint and iris of a person for the automatic identification of an individual by combining finger print and iris of a person at the matching-score level. A technique called Minutiae matching and Edge detection is used for this purpose. The performance of the proposed technique has been evaluated and accuracy has been increased by minimizing the FAR (False Acceptance Rate) and FRR (False Rejection Rate).

Eye movements are a relatively novel data source for biometric identification. When video cameras applied to eye tracking become smaller and more efficient, this data source could offer interesting opportunities for the development of eye movement biometrics. In this paper, we study primarily biometric identification as seen as a classification task of multiple classes, and secondarily biometric verification considered as binary classification. Our research is based on the saccadic eye movement signal measurements from 109 young subjects. In order to test the data measured, we use a procedure of biometric identification according to the one-versus-one (subject) principle. In a development from our previous research, which also involved biometric verification based on saccadic eye movements, we now apply another eye movement tracker device with a higher sampling frequency of 250 Hz. The results obtained are good, with correct identification rates at 80–90% at their best.

Eye movement authentication technology has been proposed as a biometric modality, which enables to authenticate a user continuously, and has the counterfeit feature because of the difficulty of the imitation. By using the eye movement authentication, it is possible to realize an automatic authentication system as long as he/she is looking at a display to operate the device. However, the authentication accuracy is still low compared to the other traditional biometric modalities, such as fingerprint, face, and iris. In this paper, we propose the novel local eye movement feature to represent local differences of the captured time-series gazing position data, and we show the proposed feature can work as a complement feature against Mel Frequency Cepstrum Coefficients(MFCC), which is based on the local phase information of eye movement data.We show the classification rate improves from 61% to 82% in the BioEye2015 dataset by using our proposed method on the best case.

Biometric recognition requires that the biometric characteristics used for the verification should be unique among individuals. The purpose of this study is to verify the individuality of finger vein, a new trait with superiority on accuracy and security. To carry out the research, we construct a large-scale finger vein database consisting of 710,399 images from 363,703 fingers. We adopt a score level fusion strategy to reduce the negative impact of algorithm deficiencies. We also design a distributed computing system for more than 83 billion impostor comparisons. The experimental results demonstrate that finger vein is sufficiently unique to distinguish one person from another in such scale.

Emerging technology for secured services is “BIOMETRIC”, term associated to identify an individual based on their physiological characteristics. Biometrics utilizes biological characteristics to recognize an individual. Authentication based on fingerprint has a lot of disadvantages such as it can be made foolproof by any unknown person. Finger vein is a non-forgeable pattern and it provides high end security as vein images are taken from live body. The pattern recognition technique which is used here is based on vein patterns present inside the finger under the surface of skin. The finger vein patterns are preprocessed, features are extracted by applying Gradient boosted feature algorithm technique and labeled using SVM classifier and they are used for authentication purpose.

Vein biometric has been used for personal identity for a long time. Still no standard stable deployed system exists. The main reason for non-availability of the stable systems is that the template is taken in non-laboratory environments. The approaches proposed are very good in lab environment but in real time scenarios do not show the claimed performance. There is an upper bound up to which a single vein biometric instance can be used efficiently. So we are proposing merging of both palm veins and finger veins to make a final template. Merging of robust features of two biometric traits will enhance the overall matching quality without much need of new hardware systems. Another solution can be to use high resolutions infrared cameras which will increase the cost of the system. So to make low cost systems low resolution cameras will be used. So our concept is to add two low resolution camera images to make a robust biometric template. We will check robustness of our merging algorithm on available standard datasets which contains the images taken from different cameras in different environments. The primary objective of this work is to provide a robust and secure multimodal vein biometric recognition system. The effective number of veins detectable is increased. Original vein biometric templates should not be stored in original format in the system for security. Thus our aim is to use the previous camera systems to make a higher efficient biometric authentication system. The proposed technique aims to increase the effective number of detectable veins through merging of more than one vein biometric instances.

Traditional vein recognition technology is usually based on the 2D infrared images of hand vein. However, the hand vein patterns extracted from 2D images are seriously distorted with hand posture changes. A point cloud matching vein recognition system based on Kernel Correlation(KC) algorithm is designed, which provides a new perspective to hand vein recognition but also suffers from posture change because KC value is sensitive to coordinate shift. In this paper, a vein point cloud registration algorithm is proposed to improve the vein authentication under multi-pose. Experiments show that the point cloud registration algorithm can effectively improve the recognition rate. After the point cloud registration, when the hand posture changes limited to the range ± 20 degrees, the system can keep more than 90% recognition rate, the corresponding error rate is only 4%.

Biometrics is the science of recognizing people based on their physical or behavioral traits such as face, fingerprints, iris, and voice. Among these characteristics, ocular biometrics has gained popularity due to the significant progress made in iris recognition. However, iris recognition is unfavorably influenced by the non-frontal gaze direction of the eye with respect to the acquisition device. In such scenarios, additional parts of the eye, such as the sclera (the white of the eye) may be of significance. In this article, we investigate the use of the sclera texture and vasculature patterns evident in the sclera as a potential biometric. Iris patterns are better discerned in the near infrared spectrum (NIR) while vasculature patterns are better discerned in the visible spectrum (RGB). Therefore, multispectral images of the eye, consisting of both NIR and RGB channels, are used in this work in order to ensure that both the iris and the vasculature patterns are imaged. The contributions of this work include: (a) the assembling of a multispectral eye database to initiate research on this topic; (b) the design of a novel algorithm for sclera segmentation based on a normalized sclera index measure; and (c) the evaluation of three different feature extraction and matching schemes on the assembled database in order to examine the potential of utilizing the sclera and the accompanying vasculature pattern as biometric cues. Experimental results convey the potential of this biometric in an ocular-based recognition system.

recognition plays an important role in the Biometric authentication. The eye lids, lashes and flash light impressions are hazard, which in turn reduces successive iris recognition rate. The proposed method includes the preprocessing of images such as image filter, morphological operations, and edge detection, which finds the exact pupil part. The proposed method uses the MLRP algorithm, to identify the exact iris layers rather than the existing methods. The key feature is extracted in iris layer. The method is applied on both left and right iris, which gives unique key between left and right eye for every person. The extracted key feature identifies the eye even in the different eye position, which gives the repeatability. The proposed method is tested with the CASIA data base iris images, which consists of left and right eye set for the different human. The proposed method reduces the FAR to 15.6% and FRR to 14%.

The recognition accuracy of a single biometric authentication system is often much reduced due to the environment, user mode and physiological defects. So a multimodal biometric approach for identity verification using two competent traits, iris and retina is proposed. This multimodal approach diminishes the drawback of single biometric system and improves the performance of an authentication system. Iris and Retina biometric recognition offers a highly reliable solution to person authentication. Iris recognition system is composed of segmentation, normalization, feature encoding and matching. Instead of using the entire iris code, only the bits that are consistent in the iris code called the Best bits are considered in the feature matching process. This reduces the computational time and storage requirements of iris code. To enhance the performance of recognition, the iris recognition process is applied to left and right irises separately and the corresponding distance scores are generated for each iris of a person. These scores are combined using the weighted sum fusion rule which further increases the recognition rate. In order to provide liveness verification for our authentication system, we also employed retinal blood vessel pattern recognition. This ensures the presence of only alive persons eliminating the possible spoofing attack. It is composed of segmentation, enhancement, feature encoding and matching. The scores from iris and retina recognition are then combined using weighted sum fusion rule, which further increases the recognition rate. To validate our approach, experiments were conducted on the iris and retina images obtained from CASIA and VARIA datasets respectively. A multimodal biometric database was constructed and then 1500 inter, intra image comparisons were made using the two datasets. The experimental results reveal that our multimodal biometric authentication system is much more reliable and precise than the single biometric approaches.

This paper proposes a novel technique to analyze the infrared vein patterns in the back of the hand for biometric purposes. The technique utilizes the minutiae features extracted from the vein patterns for recognition, which include bifurcation points and ending points. Similar to fingerprints, these feature points are used as a geometric representation of the shape of vein patterns. Analysis of a database of infrared vein patterns shows a trend that for each hand vein pattern image, there are, on average, 13 minutiae points in each vein pattern image, including 7 bifurcation and 6 ending points. The modified Hausdorff distance algorithm is proposed to evaluate the discriminating power of these minutiae for person verification purposes. Experimental results show the algorithm reaches 0% of equal error rate (EER) on the database of 47 distinct subjects, which indicates the minutiae features of the vein pattern can be used to perform personal verification tasks. The paper also presents the preprocessing techniques to obtain the minutiae points as well as in-depth study on their tolerance to processing errors, such as loss of features and geometrical displacement.

People are depending on information very much and it is becoming essential to carry out different activities every day. The degree of security in many situations becoming critical and need to be paid attention because of increase of crime rate with sophisticated technology. To increase the privacy and safety of information care must be taken to protect the information. Individual authentication is achieved using Biometric traits in different fields. As biometric authentication, there are methods using finger print, iris, and voice and so on. For the disadvantages of other biometric recognition methods, dorsal hand vein pattern has emerged as new biometric because the vascular structure is unique to every individual. The vein is the inner features of body and can't be fabricated and they are lasting. So it can be used for identification of individuals in highly secured areas. In this project, a new approach is proposed for extracting critical features from the dorsal hand vein pattern using the concepts of Walsh transform and Euclidean distance similarity measure. The proposed methodology has been tested on a dataset of 100 dorsal hand vein images and the experimental results are found to be promising.

In this paper personal verification method using finger-vein and signature is presented. Among many authentication systems finger-vein is promising as the foolproof method of automatic personal identification. Finger-vein and signature image is pre-processed and features are extracted using cross number concept and principle compound analysis. Fusion technique is used to fuse the finger vein and signature images. Then the visual cryptographic scheme is applied for the biometric template to generate the shares. The shares are stored in a separate database, and then the biometric image is revealed only when both the shares are simultaneously available. At the same time, the individual image does not reveal the identity of the biometric image. The proposed work is evaluated with evaluation metrics FAR, FRR and accuracy.

A novel non-invasive imaging technique to image the vein patterns in various parts of the hand for biometric purposes is evaluated. Two imaging methods are investigated: far-infrared (FIR) thermography and near-infrared (NIR) imaging. Experiments involving data acquisition from various parts of the hand, including the back of the hand, palm and wrist, were carried out using both imaging techniques. Analysis of the data collected shows that FIR thermography is less successful at capturing veins in the palm and wrist. FIR thermography can capture the large veins in the back of the hand, but it is sensitive to ambient temperature and humidity conditions as well as human body temperature. NIR imaging produces good quality images when capturing veins in the back of the hand, palm and wrist. NIR imaging is also more tolerant to changes in the environment and body condition but faces the problem of pattern corruption because of visible skin features being mistaken for veins. This corruption is not present in FIR imaging. An initial biometric system is investigated to test both FIR and NIR images for biometric purposes. The results show all the subjects were correctly identified, which indicates vein pattern biometrics with infrared imaging is a potentially useful biometric.

Cryptosystems based on biometrics authentication is a developing area in the field of modern security systems. The present paper deals with the application of finger veins pattern as an approach for user authentication and encryption key generation. The design of the optical capture system by near infrared is described. We propose an algorithm for the location of the vein intersection points and the quantification of the angles between the vein-branches, this information is used to generate a personal key that allows the user to encrypt information after the authentication is approved. A testing of identity verification, after subject enrollment process is presented. In order to demonstrate the potential of the proposed approach, an example of image encryption is developed. All processes: biometric capture, image preprocessing, key generation and image encryption are performed on the same embedded platform adding an important portability and diminishing the execution time. The results show an accuracy of 96.1% for the authentication system.

Finger and palm vein recognition, based on near infra-red images of the vein pattern of the finger or the palm, are promising biometric authentication methods. The main advantage of vein recognition over fingerprints is its touch-less nature, making it more robust to spoofing and more comfortable to the user. To this point, vein recognition has mainly been developed by private companies rather than by academic institutions and there are only a relatively limited number of scientific publications on the topic. This paper presents two palm vein and one finger vein imaging prototypes developed in our institution. An image database has also been acquired with each of these three prototypes.

Background/Objectives: Providing security to systems is one of the major challenges faced in day-to-day life. Biometrics plays a vital role in ensuring security. Out of the different existing recognition systems available - namely face, finger, gait, retina and so on, sclera recognition system gives out better performance. Sclera is the white part of the eye, which is unique and consistent in nature because of which it is chosen for recognition. In this paper, we analyzed the existing sclera recognition system with both human and animal eye images. Methods/Statistical Analysis: In this paper, we compared the performance of the algorithm with both human and animal eye images. The animals we considered for the algorithm analysis include deer, buffalo and lion. Human eyes are the most observable due to the presence of more sclera area. The blood vessel patterns present in the sclera region are stable over lifetime and unique person by person, thus making it appropriate for identification. The results show that the algorithm works better for human eye. Results/Findings: In this paper, we compared the extracted input vessel structure with that in the database and verify whether the input is authorized or not. It is observed that the system underperforms for animal eye images, whereas it shows an acceptable performance with human eye images. Conclusion/Application: The results prove that the algorithm works satisfactorily for identifying human. It can be incorporated with other recognition systems to build up a multimodal biometric system that can provide better security than the existing systems.

Finger vein based biometric recognition has increasingly generated interest amongst biometric researchers because of the accuracy, robustness and anti-spoofing propertie. Prior efforts the are documented in the finger vein biometrics literature have only investigated the ventral vein pattern that is formed on the lower part of the finger underneath the skin surface. This paper investigates a new finger vein biometric approach by exploring the vein pattern that is present in the dorsal finger region. Thus, the dorsal finger vein pattern can be used as an independent biometric characteristic useful for the recognition of the target subject. We presented a complete automated approach with the key steps of image capturing, Region of Interest (ROI) extraction, pre-processing to enhance the vein pattern, feature extraction and comparison. This paper also introduces a new database of dorsal finger vein patterns from 125 subjects that resulted in 500 unique fingers with 10 samples each that results in a total of 5000 dorsal finger vein samples. Extensive experiments carried out on our new dorsal finger vein database achieve promising accuracy and thereby provide new insights on this new biometric approach.

Biometric recognition is noteworthy method for recognition of person in recent years. Here, a common concern is biometric security which is the privacy issues derived from storage and misuses of the template data. In order to handle this issue, researches have proposed different algorithms to be confronted by security of biometric systems. Two major ways are, (1) Encryption, and (2) watermarking by securing biometric images and templates. In this paper, we utilise a watermarking technology to improve the template security in biometric authentication. According to, two modalities such as, iris and hand vein is taken to preserve the characteristics of liveliness and permanency. Our proposed technique for embedding of iris data to hand vein images using watermarking technology to improve template protection in biometric recognition is done based on the following steps, i) pre-processing of iris and hand vein images, ii) iris template extraction, iii) Vein extraction, iv) Embedding of iris pattern to vein images based on region of interest, v) Storing embedded images. In the recognition phase, iris pattern is extracted from the embedded image and then, matching is done with query images. The final decision of authentication is done based on the product rule-based score level fusion. The implementation is done using MATLAB and the performance of the technique is analysed with FAR, FRR and accuracy.

Traditional authentication (identity verification) systems, used to gain access to a private area in a building or to data stored in a computer, are based on something the user has (an authentication card, a magnetic key) or something the user knows (a password, an identification code). However, emerging technologies allow for more reliable and comfortable user authentication methods, most of them based on biometric parameters. Much work could be found in the literature about biometric-based authentication, using parameters like iris, voice, fingerprints, face characteristics, and others. In this work a novel authentication method is presented and preliminary results are shown. The biometric parameter employed for the authentication is the retinal vessel tree, acquired through retinal digital images, i.e., photographs of the fundus of the eye. It has already been asserted by expert clinicians that the configuration of the retinal vessels is unique for each individual and that it does not vary during his life, so it is a very well-suited identification characteristic. Before the verification process can be executed, a registration step is required to align both the reference image and the picture to be verified. A fast and reliable registration method is used to perform this step, so that the whole authentication process takes about 0.3 s.

We propose a method of personal identification based on finger-vein patterns. An image of a finger captured under infrared light contains not only the vein pattern but also irregular shading produced by the various thicknesses of the finger bones and muscles. The proposed method extracts the finger-vein pattern from the unclear image by using line tracking that starts from various positions. Experimental results show that it achieves robust pattern extraction, and the equal error rate was 0.145% in personal identification.

A novel approach to personal verification using the thermal images of palm-dorsa vein patterns is presented in this paper. The characteristics of the proposed method are that no prior knowledge about the objects is necessary and the parameters can be set automatically. In our work, an infrared (IR) camera is adopted as the input device to capture the thermal images of the palm-dorsa. In the proposed approach, two of the finger webs are automatically selected as the datum points to define the region of interest (ROI) on the thermal images. Within each ROI, feature points of the vein patterns (FPVPs) are extracted by modifying the basic tool of watershed transformation based on the properties of thermal images. According to the heat conduction law (the Fourier law), multiple features can be extracted from each FPVP for verification. Multiresolution representations of images with FPVPs are obtained using multiple multiresolution filters (MRFs) that extract the dominant points by filtering miscellaneous features for each FPVP. A hierarchical integrating function is then applied to integrate multiple features and multiresolution representations. The former is integrated by an inter-to-intra personal variation ratio and the latter is integrated by a positive Boolean function. We also introduce a logical and reasonable method to select a trained threshold for verification. Experiments were conducted using the thermal images of palm-dorsas and the results are satisfactory with an acceptable accuracy rate (FRR:2.3% and FAR:2.3%). The experimental results demonstrate that our proposed approach is valid and effective for vein-pattern verification.

A wide variety of systems requires reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user and no one else. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor. Biometric recognition, or, simply, biometrics, refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. By using biometrics, it is possible to confirm or establish an individual's identity based on "who she is", rather than by "what she possesses" (e.g., an ID card) or "what she remembers" (e.g., a password). We give a brief overview of the field of biometrics and summarize some of its advantages, disadvantages, strengths, limitations, and related privacy concerns.

The beauty of eye recognition system that it is used in automatic identifying and verifies a human weather from digital images or video source. There are various behaviors of the eye such as the color of the iris, size of pupil and shape of the eye. This study represents the analysis, design and implementation of a system for recognition of eye imaging. All the eye images that had been captured from the webcam in RGB format must through several techniques before it can be input for the pattern and recognition processes. The result shows that the final value of weight and bias after complete training 6 eye images for one subject is memorized by the neural network system and be the reference value of the weight and bias for the testing part. The target classifies to 5 different types for 5 subjects. The eye images can recognize the subject based on the target that had been set earlier during the training process. When the values between new eye image and the eye image in the database are almost equal, it is considered the eye image is matched.

This paper presents an efficient system for palm-dorsa vein pattern based recognition system. It can handle efficiently the problem of false palm-dorsa veins which can be created by many ways such as ink, tattoos, artificial vein pattern paper fixed on the palm-dorsa. Hand-dorsa images acquired under visible and infrared lights are used. Since vein pattern from infrared light has spurious and genuine vein pattern, spurious vein pattern is removed from it by using vein pattern from visible light. It has been tested on 600 visible and 600 infrared hand-dorsa images. Experimental results indicate that the proposed system performs efficiently.

A Multimodal authentication system with fusion at decision level employing Iris and Finger Vein is developed. This paper also proposes a novel feature extraction technique for finger vein and iris traits. This approach combines the decisions from the individual modalities using the conventional AND rule. Experimental results demonstrate the effectiveness of the proposed multimodal authentication system for accurate person authentication.

Biometric based identifications are widely adopted for personnel identification. The unimodal recognition systems currently suffer from noisy data, spoofing attacks, biometric sensor data quality and many more. Robust personnel recognition considering multimodal biometric traits can be achieved. This paper introduces the Multimodal Personnel Authentication using Finger vein and Face Images (MPAFFI) considering the Finger Vein and Face biometric traits. The use of Magnitude and Phase features obtained from Gabor Kernels is considered to define the biometric traits of personnel. The biometric feature space is reduced using Fischer Score and Linear Discriminate Analysis. Personnel recognition is achieved using the weighted K-nearest neighbor classifier. The experimental study presented in the paper considers the (Group of Machine Learning and Applications, Shandong University-Homologous Multimodal Traits) SDUMLA — HMT multimodal biometric dataset. The performance of the MPAFFI is compared with the existing recognition systems and the performance improvement is proved through the results obtained.

Biometrics is a technique of identity verification that is based on the principle of measurable physiological characteristics like vein patterns from sclera region. Sclera vein is a new biometric recognition method for human identification. Sclera vessel pattern's images are saturated and the arrangement of vessel patterns is multi layered and also it is quite complex to extract the features of vein from sclera. In order eliminate high frequency noise components present in sclera, low pass filter is used. To extract the features, a set of vessel segments combine to create Y shaped branches often belonging to same sclera layer. Mapping scheme is based on Y shaped structure in the sclera that is observed to be more stable feature and is used to efficiently eliminate the most unlikely matches. The observational result shows that the proposed method can dramatically provide more secure authentication without compromising the recognition accuracy.

The vascular pattern of the finger has emerged as a promising new biometric modality, characterized by very low error rates, good spoofing resistance and user convenience when compared with other existing biometric modalities. In this paper, we present a new sensor design that is not only cost effective but also robust enough to capture the finger vascular (or finger vein) pattern. We evaluate the previously proposed finger vein verification approaches and also propose a new scheme that illustrates its superiority over existing approaches. Extensive experiments are carried out on the newly developed database constructed using our newly designed finger vein imaging sensor that comprises of 1780 finger vein samples corresponding to 89 unique fingers from 32 subjects. The data is collected in two different scenarios shows the efficacy of the proposed sensor as well as the proposed scheme. I. INTRODUCTION Biometric system involves in establishing the human iden- tity based on the physical and behavioural characteristics. The adoptability of the biometric system in the large scale appli- cations like India's UIDAI (Aadhar) and UAE's port of entry shows the effectiveness of biometrics for real time identity solutions. Among the various biometric characteristics that are available, the finger vascular pattern (or finger vein) biometrics has received a substantial attention from both academia and industry. The finger vascular pattern system uses information about the blood vessel structures inside the finger. As a con- sequence, it offers a wide range of advantages including: (1) More convenient and ease to capture (2) Unique patterns and high verification rate (3) Difficult to spoof. However, capturing a high quality sample of finger vein is still a challenging task as it demands the adequate amount of light to penetrate or reflect from the finger. Further, brightness within the finger may also vary as finger structure is not equally thick. These factors motivate us to explore a new imaging device to reliably observe the prominent information from the fingervein that in turn can be used to build a biometric system using finger vein samples to achieve robust identity verification.

Nowadays, many applications require authentication to identify human before giving acce ss to the resources. With new advances in technology, biometric technique of human authentication, which uses the human iris pattern, is well suited. Biometrics intends to identify a user by his/her physical and or/ behavioural characteristics. Among all biometric techniques, Iris recognition stands out as its error rates are lowest. Iris recognition includes eye imaging, localization, normalization, verification etc. This paper presents an overview of the fundamentals of biometric identification and tries to find out the effect of masking the normalized iris image on recognition rates. With the advancement in technology, danger of security breach has also increased. To overcome this, there is a need to design a security system which is fast and very accurate. This system can be designed on two bases: first is data based in which user has to carry some cards or remember passwords, second is biometric based in which identification is done on th e basis of physiological, behavioral or chemical characteristics of human body. Systems designed on the basis of human traits like fingerprint, iris, retina etc are known as Biometric Systems. Working of such systems is based on signal and image processing techniques. Biometrics provides more security, as they are difficult to replicate and steal. With the increasing demands of personal/private security iris recognition has become one of the highly reliable biometric technologies as compared to others due to its benefits like less operational time, simplicity in handling and accuracy. The basic of every biometric system is to extract the prominent features from the input signature via image with the application of some wavelet transform, fuzzy logic, neural network etc like algorithms. Biometric system uses two processes namely enrollment and verification to authenticate the identity. During enrolment step, an image is captured by the system and a template is created. These templates are then stored collectively in database. During the verification or authentication step, when a user gives a sample of its biometric data, it is compared with stored template and on the basis of matching score a decision is made. So Iris based biometric security system consists of various steps, that make up an architectural framework, namely Image Acquisition, Localization, Normalization, Image Enhancement

This document describes the software framework of an ocular biometric system. The framework encompasses several interconnected components that allow an end-user to perform biometric enrollment, verification, and identification with most common eye tracking devices. The framework, written in C#, includes multiple state-of-the-art biometric algorithms and information fusion techniques, and can be easily extended to utilize new biometric techniques and eye tracking devices.