Biometric Recognition System Research Articles

Non-Invertible cancellable fingerprint template for fingerprint biometric

Fingerprint biometric has emerged as a highly successful biometric recognition system and find its application in the diverse domain. The fingerprint template database needs to be protected from unauthorized access. If an adversary has acquired the unprotected fingerprint template, then he/she may reconstruct original fingerprint which can be used for unauthorized access of all the systems where the same fingerprint has been used. Hence, securing the fingerprint template is required. In this paper, a non-invertible cancellable fingerprint template based on information extracted from the Delaunay triangulation of minutiae points is proposed. For making the template cancellable a user-specific key of a random binary string is used. The proposed template is revocable, diverse, secure and also gives a good recognition accuracy. The proposed template is tested with a standard FVC2002 database and gives good results as compared with the latest methods.

Enhancing Fixed Size Palmprint Region of Interest (ROI) Extraction Algorithm for Personal Identification

Identification and verification are the fundamental process in biometrics recognition system. Research indicates that palmprint, as one of the biometric recognitions system is commonly used for human identification. It is because there are many features and information contained inside the palmprint that can be used in the identification process. However, only a small region of the palmprint can be extracted using the existing palmprint region of interest (ROI) extraction algorithms. This has become a problem for identification systems due to negligible and loss of important features which are located outside the ROI. Hence, it is a necessity to improve the palmprint ROI extraction algorithm whereby bigger palmprint ROI can be extracted using this algorithm. Therefore, a larger fixed size extraction algorithm for palmprint ROI is proposed where the extraction region is larger so that more important identification features can be captured inside these ROIs. The performance between proposed and existing extraction algorithms are tested based on two characteristics which are the palmprint ROI extraction area and the comparison of feature creases extracted in a palmprint ROI. The results show that 300x300 fixed size ROI is able to capture 13 out of 14 creases attributes for palmprint identification. This implies that the proposed extraction algorithm shows a promising method of extraction as compared to the existing algorithms.

Design of Multi-Biometrics for Fake Fingerprint Detection through Body Odor

This work proposes the modernistic multi-biometric recognition system for detecting artificial fingerprints and new biometric recognition system to use it in some real-time scenarios. In the recent studies of multi-biometrics, the usage of fingerprint and body odor recognition system stays untouched. This proposed design of a multi-biometric system includes a body odor recognition system along with a fingerprint recognition system that will improve the results in terms of accuracy. The reason behind proposing this model is to detect artificial fingerprints by differentiating the odor of human skin from other materials that are employed in the preparation of artificial fingerprints. This multi-biometric system can be used in forensic labs to identify criminals and to improve the standards of security in authentication of an individual. This multi-biometric system will completely eradicate the use of fake fingerprints and this proposed work will make a remarkable place in real-time applications and the history of multi-biometric systems.

Experimental Validation of a Reliable Palmprint Recognition System Based on 2D Ultrasound Images

Ultrasound has been trialed in biometric recognition systems for many years, and at present different types of ultrasound fingerprint readers are being produced and integrated in portable devices. An important merit of the ultrasound is its ability to image the internal structure of the hand, which can guarantee improved recognition rates and resistance to spoofing attacks. In addition, ambient noise like changes of illumination, humidity, or temperature, as well as oil or ink stains on the skin do not affect the ultrasound image. In this work, a palmprint recognition system based on ultrasound images is proposed and experimentally validated. The system uses a gel pad to obtain acoustic coupling between the ultrasound probe and the user’s hand. The collected volumetric image is processed to extract 2D palmprints at various under-skin depths. Features are extracted from one of these 2D palmprints using a line-based procedure. Recognition performances of the proposed system were evaluated by performing both verification and identification experiments on a home-made database containing 281 samples collected from 32 different volunteers. An equal error rate of 0.38% and an identification rate of 100% were achieved. These results are very satisfactory, even if obtained with a relatively small database. A discussion on the causes of bad acquisitions is also presented, and a possible solution to further optimize the acquisition system is suggested.

A highly-verified biometric recognition system using an ultra-speed specifically-developed finger vein sensor

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.

A Multi Biometric IRIS Recognition System based on a Profound Learning Method

Biometrics is the estimation of natural qualities which are one of a kind to a person for recognizing and confirming the person. The estimations incorporate fingerprints, retinal outputs, iris checks, voice designs, facial qualities, palm prints, and so forth.., Biometric frameworks have been especially effective in distinguishing an obscure individual via looking through a database of attributes and by confirming the case of a person by contrasting his/her trademark with that put away in a database. To expand the heartiness of the framework and to make it more secure, different attributes of a similar individual are utilized. This is alluded to as multimodal biometrics. In this paper we talked about a portion of the multimodal biometric frameworks. Here a bi-modular biometric acknowledgment framework in light of iris, palm-print. Wavelet and curve let change and Gabor-edge channel are utilized to extricate includes in various weighing machine moreover introductions starting iris as well as palm print, finer points taking out in addition to arrangement is utilized in favour of coordinating. diverse combination calculations together with achieve based, positionbased plus choice depend on techniques are utilized to-join the consequences of two constituents. We additionally recommend another rank-based combination calculation Bio Maximum Inverse Rank (BMIR) which is vigorous as for varieties in scores and furthermore awful positioning from a module. IITD iris databases and CASIA datasets for palm print and unique mark are utilized in this investigation. The examinations demonstrate the adequacy of our combination strategy, profound learning, neural systems and our Bi-modular biometric acknowledgment framework in contrast with existing multi-modular acknowledgment frameworks.

Bimodal biometric pattern recognition system based on fusion of iris and palmprint using multi-resolution approach

A biometric pattern recognition system is a pattern recognition system which uses the biological traits to recognize individuals. Single biometric systems may not possess the required characteristics like permanence, acceptability and circumvention. In general, the performance of the unimodal biometrics systems is degraded due to noisy data acquisition from sensor, intra-class and inter-class similarities. Several such restrictions are removed when multiple sources of information are used. In this paper, a bimodal biometric system designed from iris pattern and palmprint pattern is described. The feature extractor is created from the wavelet packet analysis, and classifier is created based on neural network. Using wavelet packets and gray-level spatial dependence matrix, the iris code vector is constructed. With the help of Gabor wavelets and gray-level spatial dependence matrix, the palmprint code vector is computed. The features extracted from iris pattern and palmprint pattern are fused by feature-level fusion into a multimodal pattern vector of size 1408 bits. The recognition rate achieved by the LVQ neural network is 94.50%. This system can complete recognition in 15.25 microseconds so that it can be made use for implementing real-time recognition tasks.

Ear anti-spoofing against print attacks using three-level fusion of image quality measures

Ear biometric recognition systems have been developed in the last decades. Consequently, the attacks to these biometrics security systems became inevitable. Recently, image quality assessment methods are one of the popular methods used for countering spoof attacks. There is no ear anti-spoofing system proposed in the literature. Therefore, in order to counter spoof attacks on ear biometric recognition systems, an efficient method is proposed in this study. This proposed method is based on full-reference and no-reference image quality assessment methods. Three-level score-level fusion and decision-level fusion techniques are employed in the solution of the proposed method. The experiments are conducted by preparing the printed photo attack images of AMI and UBEAR ear databases. The proposed system significantly recognizes real and fake ear images compared to the other systems implemented in this study. Half total error rates of the proposed system using printed photo attack images are compared with the error rates of the other implemented systems employing various fusion techniques for ear anti-spoofing. Additionally, the proposed system is compared with the state-of-the-art anti-spoofing systems and CNN-based deep learning anti-spoofing systems against print attacks on other biometric traits since this is the first study presenting ear anti-spoofing systems using score-level and decision-level fusion of image quality assessment methods.

Biometric-Based Attendance Tracking System for Education Sectors: A Literature Survey on Hardware Requirements

The application of biometric recognition in personal authentication enables the growth of this technology to be employed in various domains. The implementation of biometric recognition systems can be based on physical or behavioral characteristics, such as the iris, voice, fingerprint, and face. Currently, the attendance tracking system based on biometric recognition for education sectors is still underutilized, thus providing a good opportunity to carry out interesting research in this area. As evidenced in a typical classroom, educators tend to take the attendance of their students by using conventional methods such as by calling out names or signing off an attendance sheet. Yet, these types of methods are proved to be time consuming and tedious, and sometimes, fraud occurs. As a result, significant progress had been made to mark attendance automatically by making use of biometric recognition. This progress enables a new and more advanced biometric-based attendance system being developed over the past ten years. The setting-up of biometric-based attendance systems requires both software and hardware components. Since the software and hardware sections are too broad to be discussed in one paper, this literature survey only provides an overview of the types of hardware used. Emphasis is then placed on the microcontroller platform, biometric sensor, communication channel, database storage, and other components in order to assist future researchers in designing the hardware part of biometric-based attendance systems.

Liveness Detection with Opencv

Biometric system is widely used to recognize the authorized person based on either behavioural characteristics or physical. But this can be spoofed using various traits. Spoofing attack is nothing but attacking or harming biometric recognition system using security features to use system without permission of authorized user. Images or video of person can be easily available from social media or can be easily captured from some distance. Consider what would happen if a nefarious user tried to purposely circumvent your face recognition system. Such a user could try to hold up a photo of another person. Maybe they even have a photo or video on their smart phone that they could hold up to the camera responsible for performing face recognition .In the project, we have tried spotting these “fake” versus “real/legitimate” faces and how we could apply anti-face spoofing algorithms into our facial recognition applications by applying live ness detection[1] with Open CV.

A Synthetic Fusion Rule Based on FLDA and PCA for Iris Recognition Using 1D Log-Gabor Filter

Iris recognition is one of the most useful methods to identify or verify people in biometric recognition systems. Iris patterns contain many features that distinguish people from each other. In this paper, a novel iris recognition method is proposed based on the fusion of Fisher Linear Discriminate Analysis (FLDA) with embedding Principal Component Analysis (PCA) method. In this work, firstly we use 1D Log-Gabor to elicit the iris features from an approximation part. Secondly, we obtain an appropriate degree of clarity for the iris with fusion of FLDA/PCA to eliminate the optical reflections on the iris image. Experiments of our proposed algorithm are performed on the CASIA V1 database. The results of our proposed approach show a good performance with recognition rate up to 99.99%.

Phacoemulsification Cataract Surgery Affects the Discriminative Capacity of Iris Pattern Recognition

Cataract is a common ophthalmic disorder and the leading cause of blindness worldwide. While cataract is cured via surgical procedures, its impact on iris based biometric recognition has not been effectively studied. The key objective of this research is to assess the effect of cataract surgery on the iris texture pattern as a means of personal authentication. We prepare and release the IIITD Cataract Surgery Database (CaSD) captured from 132 cataract patients using three commercial iris sensors. A non-comparative non-randomized cohort study is performed on the iris texture patterns in CaSD and authentication performance is studied using three biometric recognition systems. Performance is lower when matching pre-operative images to post-operative images (74.69 ± 9.77%) as compared to matching pre-operative images to pre-operative images (93.42 ± 1.76%). 100% recognition performance is observed on a control-group of healthy irises from 68 subjects. Authentication performance improves if cataract affected subjects are re-enrolled in the system, though re-enrollment does not ensure performance at par with pre-operative scenarios (86.67 ± 5.64%). The results indicate that cataract surgery affects the discriminative nature of the iris texture pattern. This finding raises concerns about the reliability of iris-based biometric recognition systems in the context of subjects undergoing cataract surgery.

Ear Detection and Localization with Convolutional Neural Networks in Natural Images and Videos

The difficulty in precisely detecting and locating an ear within an image is the first step to tackle in an ear-based biometric recognition system, a challenge which increases in difficulty when working with variable photographic conditions. This is in part due to the irregular shapes of human ears, but also because of variable lighting conditions and the ever changing profile shape of an ear’s projection when photographed. An ear detection system involving multiple convolutional neural networks and a detection grouping algorithm is proposed to identify the presence and location of an ear in a given input image. The proposed method matches the performance of other methods when analyzed against clean and purpose-shot photographs, reaching an accuracy of upwards of 98%, but clearly outperforms them with a rate of over 86% when the system is subjected to non-cooperative natural images where the subject appears in challenging orientations and photographic conditions.

Probability-Possibility Theories Based Iris Biometric Recognition System

The performance and robustness of the iris-based recognition systems still suffer from imperfection in the biometric information. This paper makes an attempt to address these imperfections and deals with important problem for real system. We proposed a new method for iris recognition system based on uncertainty theories to treat imperfection iris feature. Several factors cause different types of degradation in iris data such as the poor quality of the acquired pictures, the partial occlusion of the iris region due to light spots, or lenses, eyeglasses, hair or eyelids, and adverse illumination and/or contrast. All of these factors are open problems in the field of iris recognition and affect the performance of iris segmentation, its feature extraction or decision making process, and appear as imperfections in the extracted iris feature. The aim of our experiments is to model the variability and ambiguity in the iris data with the uncertainty theories. This paper illustrates the importance of the use of this theory for modeling or/and treating encountered imperfections. Several comparative experiments are conducted on two subsets of the CASIA-V4 iris image database namely Interval and Synthetic. Compared to a typical iris recognition system relying on the uncertainty theories, experimental results show that our proposed model improves the iris recognition system in terms of Equal Error Rates (EER), Area Under the receiver operating characteristics Curve (AUC) and Accuracy Recognition Rate (ARR) statistics.

Multispectral Palmprint Recognition: A Survey and Comparative Study

Multispectral palmprint recognition has been investigated for many problems and applications over the last decade. It has become one of the most well-known biometric recognition systems. Its success is due to the rich features that can be extracted and exploited from the multispectral images of palmprint captured within specific wavelength ranges across the electromagnetic spectrum. This paper provides an overview of recent state-of-the-art multispectral palmprint approaches for person recognition. The approaches surveyed are discussed by describing, in particular, their feature extraction, feature fusion, matching and decision algorithms. Finally, a comparative study to evaluate their performances for both verification and identification modes is addressed.

Deep multimodal biometric recognition using contourlet derivative weighted rank fusion with human face, fingerprint and iris images

ABSTRACT The goal of multimodal biometric recognition system is to make a decision by identifying their physiological behavioural traits. Nevertheless, the decision-making process by biometric recognition system can be extremely complex due to high dimension unimodal features in temporal domain. This paper explains a deep multimodal biometric system for human recognition using three traits, face, fingerprint and iris. With the objective of reducing the feature vector dimension in the temporal domain, first pre-processing is performed using Contourlet Transform Model. Next, Local Derivative Ternary Pattern model is applied to the pre-processed features where the feature discrimination power is improved by obtaining the coefficients that has maximum variation across pre-processed multimodality features, therefore improving recognition accuracy. Weighted Rank Level Fusion is applied to the extracted multimodal features, that efficiently combine the biometric matching scores from several modalities (i.e. face, fingerprint and iris). Finally, a deep learning framework is presented for improving the recognition rate of the multimodal biometric system in temporal domain. The results of the proposed multimodal biometric recognition framework were compared with other multimodal methods. Out of these comparisons, the multimodal face, fingerprint and iris fusion offers significant improvements in the recognition rate of the suggested multimodal biometric system.

Accuracy enhancement of biometric recognition using iterative weights optimization algorithm

A new approach is proposed to quantitatively evaluate the binary detection performance of the biometric personal recognition systems. The importance of correlation between the overall detection performance and the area under the genuine acceptance rate (GAR) versus false acceptance rate (FAR) graph, commonly known as receiver operating characteristics (ROC) is recognized. Using the ROC curve, relation between GARmin and minimum recognition accuracy is derived, particularly for high security applications (HSA). Finally, effectiveness of any binary recognition system is predicted using three important parameters, namely GARmin, the time required for recognition and computational complexity of the computer processing system. The palm print (PP) modality is used to validate the theoretical basis. It is observed that by combining different useful feature-extraction techniques, it is possible to improve the system accuracy. An optimum algorithm to appropriately choose weights has been suggested, which iteratively enhances the system accuracy. This also improves the effectiveness of the system.

Ultrasound Systems for Biometric Recognition.

Biometric recognition systems are finding applications in more and more civilian fields because they proved to be reliable and accurate. Among the other technologies, ultrasound has the main merit of acquiring 3D images, which allows it to provide more distinctive features and gives it a high resistance to spoof attacks. This work reviews main research activities devoted to the study and development of ultrasound sensors and systems for biometric recognition purposes. Several transducer technologies and different ultrasound techniques have been experimented on for imaging biometric characteristics like fingerprints, hand vein pattern, palmprint, and hand geometry. In the paper, basic concepts on ultrasound imaging techniques and technologies are briefly recalled and, subsequently, research studies are classified according to the kind of technique used for collecting the ultrasound image. Overall, the overview demonstrates that ultrasound may compete with other technologies in the expanding market of biometrics, as the different commercial fingerprint sensors integrated in portable electronic devices like smartphones or tablets demonstrate.

Pattern Mining Approaches Used in Sensor-Based Biometric Recognition: A Review

Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing, and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being—acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition, and/or classification, clustering, and validation. A significant number of approaches from image processing, pattern identification, and machine learning have been used to process sensor data. This paper aims to deliver the state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems.

Novel Local Coding Algorithm for Finger Multimodal Feature Description and Recognition.

Recently, finger-based biometrics, including fingerprint (FP), finger-vein (FV) and finger-knuckle-print (FKP) with high convenience and user friendliness, have attracted much attention for personal identification. The features expression which is insensitive to illumination and pose variation are beneficial for finger trimodal recognition performance improvement. Therefore, exploring suitable method of reliable feature description is of great significance for developing finger-based biometric recognition system. In this paper, we first propose a correction approach for dealing with the pose inconsistency among the finger trimodal images, and then introduce a novel local coding-based feature expression method to further implement feature fusion of FP, FV, and FKP traits. First, for the coding scheme a bank of oriented Gabor filters is used for direction feature enhancement in finger images. Then, a generalized symmetric local graph structure (GSLGS) is developed to fully express the position and orientation relationships among neighborhood pixels. Experimental results on our own-built finger trimodal database show that the proposed coding-based approach achieves excellent performance in improving the matching accuracy and recognition efficiency.