Biometrics-based Identification Systems Research Articles

An Efficient Clustering-Based Segmentation Approach for Biometric Image

Background: Image analysis plays a vital role in the biometric identification system. To achieve the effective outcome of any biometric identification system, the inputted biometric image taken should be of fine quality as it greatly impacts the decision. Image segmentation is a significant aspect of image analysis that must be carried out for enhancing the quality of an image. It efficiently differentiates the foreground and background region of the inputted biometric image and facilitates further image processing simply by providing a segmented binary image which is more coherent to the system. Objective: We present an efficient clustering based image segmentation approach to obtain the quality segmented binary image that further processed to get the quality decision in biometric based identification system. Method: A centre of mass based centroid clustering approach for image segmentation has been proposed to perform binarization of an image so as the adequate and operative results can be found. Result: The performance of the proposed approach has been applied on different sets of biometric data set having different number of hand images. Resultant this approach provides the sharp and lucid images so that good enough and effective intended results can be obtained. Conclusion: The centroid based clustering approach for image segmentation outperform the existing clustering approach. In order to measure the quality of segmented binary image three statistical performance parameters are used: PSNR, SUMD and Time Elapsed (sec).

An investigation of the predictability of the Brazilian three-modal hand-based behavioural biometric: a feature selection and feature-fusion approach

New security systems, methods or techniques need to have their performance evaluated in conditions that closely resemble a real-life situation. The effectiveness with which individual identity can be predicted in different scenarios can benefit from seeking a broad base of identity evidence. Many approaches to the implementation of biometric-based identification systems are possible, and different configurations are likely to generate significantly different operational characteristics. The choice of implementational structure is, therefore, very dependent on the performance criteria, which is most important in any particular task scenario. The issue of improving performance can be addressed in many ways, but system configurations based on integrating different information sources are widely adopted in order to achieve this. Thus, understanding how each data information can influence performance is very important. The use of similar modalities may imply that we can use the same features. However, there is no indication that very similar (such as keyboard and touch keystroke dynamics, for example) basic biometrics will perform well using the same set of features. In this paper, we will evaluate the merits of using a three-modal hand-based biometric database for user prediction focusing on feature selection as the main investigation point. To the best of our knowledge, this is the first thought-out analysis of a database with three modalities that were collected from the same users, containing keyboard keystroke, touch keystroke and handwritten signature. First, we will investigate how the keystroke modalities perform, and then, we will add the signature in order to understand if there is any improvement in the results. We have used a wide range of techniques for feature selection that includes filters and wrappers (genetic algorithms), and we have validated our findings using a clustering technique.

Face geometry as a biometric-based identification system

In this paper, we present a method for identifying a person with a biometric-based on face geometry. This person identification system design has been done using face biometrics by implementing feature extraction of distances between facial features. Face biometrics were chosen because they have unique characteristics and do not change in each person. In this method, a system analysis that separates the face image into face components, including the eyes, nose, and mouth on the face image is taken from the front view position. Each component of the feature that has been detected is measured by its distance to form the face semantics. The steps taken in image processing begin with determining the region of interest (ROI). Furthermore, the results of cropping ROI feature extraction are done to get the eye, nose and mouth. After that, a centroid is determined for each feature so that the distance between features can be calculated using the equation of the distance between the two coordinates, so we get 8 distances as matching parameters. The matching process is done using euclidean distance, which is calculating the smallest distance between the test image and the database. Based on research results, this system can be used to identify someone using face biometrics with fixed poses and expressions. The accuracy of this system is 100 percent

Finger Edge Contour Perimeter as a Biometric Based Identification System

Generally, personal identification system is still done by conventional techniques, such as using ID cards, passwords or PIN numbers. These systems is easily lost, damaged or forgotten. In this paper we present a new method for identifying a person with a biometric based on palm of the hand. This system does not require a peg like an identification system based on existing hand geometry. Phase of development are: first, the palm image scanned in the RGB format is converted into a gray level. Then the image is converted to binary by giving the correct threshold value. From the binary image, we carry out the edge detection process to get the contour perimeter of the palm. From the contour perimeter of the palm, we measure the length of 8 edges contour perimeter of the fingers i.e. the right edge of the thumb, the left edge of the index finger, the right edge of the index finger, the left edge of middle finger, the right edge of middle finger, the left edge of pinkie, the right edge of pinkie, and the left edge of ring finger. The identification process is performed by minimum distance, which is the smallest euclidean distance value associated with the test image and database. The experimental results giving good performance with a success of up to 90%.

Research of Face Recognition with Fisher Linear Discriminant

Face identification systems are developing rapidly, and these developments drive the advancement of biometric-based identification systems that have high accuracy. However, to develop a good face recognition system and to have high accuracy is something that’s hard to find. Human faces have diverse expressions and attribute changes such as eyeglasses, mustache, beard and others. Fisher Linear Discriminant (FLD) is a class-specific method that distinguishes facial image images into classes and also creates distance between classes and intra classes so as to produce better classification.

Using biometric-based identification systems in Brazil: A review on low cost fingerprint techniques on-the-go

Automatic authentication has become an essential service in several public areas. However, although the technology related with this kind of service has evolved, the price tag of its use is not affordable for most countries. In the so-called “under developed” counties, such as Brazil, South Africa and India, for example, registration systems are often paper-based and/or cover only a fraction of the population. Thus, the reality is that there is an increasing gap into the usage of such technologies amongst different countries and it can be a factor that makes development more difficult and, therefore, less inclusive. One of the main technologies used for automatic identity prediction is based on biometrics analysis, which can distinguish physical or behavioural features to help overcome the traditional paper-based identity systems. Despite the limitations already mentioned, Brazil is known to have introduced several different uses of biometric-based technologies for authentication. However, the use of these technologies is not always ideal and, since the population size is a key factor, it is essential to select the most affordable option which is not necessarily the most adequate for the country's needs. This paper will focus on establishing what biometric-based solutions exist in Brazil today, highlighting the main challenges, as well as briefly proposing a new prototype for mobile fingerprint acquisition.

Gabor Filter and Texture based Features for Palmprint Recognition

In this paper, we propose an efficient personal identification system based on palmprint recognition. Palmprint is widely used in biometric-based identification system. Palmprint is robust and obtained in a simple way. After extracting region of interest (ROI), the ROI is passed through Gabor filters with different wavelengths and orientations. Then, binarized statistical image features (BSIF) of phase of outputs of Gabor filters are obtained. Different BSIF codes are combined together and then, the histogram of final BSIF code is calculated. Efficient features from histogram are calculated and are given to the K-nearest neighbor (KNN) classifier to perform personal identification. Experimental results on PolyU database demonstrate that proposed algorithm achieves the higher accuracy than the recently proposed algorithms.

English

With the fast development of video hardware and software in recent years, intelligent video systems have been widely used in industry, transportation, security, etc. at the same time, a lot of biometric technologies, comprising automated methods for uniquely recognizing people based on their physical or behavioural traits, such as face, fingerprint, palm print, finger-knuckle-print, gait, are also based on video or image analysis. Iris recognition is emerging as one of the important methods of biometrics-based identification systems. Several crucial factors Gait Keystroke pattern Signature of iris biometrics include rich and unique textures, non-invasiveness, and stability of iris pattern throughout the person’s lifetime, public acceptance, and availability of user friendly capturing devices. These factors have attracted the researchers to work in this evolving field over the past decade. The iris recognition consists of iris localization, normalization, encoding and comparison. In this paper, segmentation of periocular features and the encoding part of iris recognition is analysed.

Dorsal hand vein recognition based on directional filter bank

With the increasing needs of security systems, vein recognition is reliable as one of the important solutions for biometrics-based identification systems. The obvious and stable line-feature-based approach can be used to clearly describe dorsal hand vein patterns for personal identification. In this paper, a directional filter bank involving different orientations is designed to extract vein patterns and the minimum directional code is employed to encode line-based vein features into binary code. In addition, there are many non-vein areas in the vein image, which are not meaningful for vein recognition. To improve accuracy, the non-vein areas are detected by evaluating the variance of the minimum directional filtering response image and are considered as non-orientation code. In total, 4,280 dorsal hand vein images from 214 persons are used to validate the proposed dorsal hand vein recognition approach. A high accuracy (\(>\)99 %) and low equal error rate (0.54 %) were obtained using the proposed approach, which shows that the approach is feasible and effective for dorsal hand vein recognition.

Gaussian directional pattern for dorsal hand vein recognition

With the increasing needs in security systems, hand vein recognition is reliable as one of the important solutions for biometrics-based identification systems. This work presents an effective approach for dorsal hand vein recognition by analysing vein patterns. The methodology involves extraction of dorsal hand vein features based on Gaussian filter. In order to obtain effective pattern of dorsal hand vein vascular, we propose an innovative and robust Gaussian directional filter method to extract the dorsal hand vein patterns and to encode the vein features in binary code. A new coding scheme, namely Gaussian directional binary code, is then proposed for dorsal hand vein recognition. To evaluate the efficacy of the proposed approach, the normalised Hamming distance used in recognition is adopted. A total of 5120 dorsal hand vein images were collected from 256 persons to verify the validity of the proposed dorsal hand vein recognition approach. High accuracies (>99%) and low equal error rate (0·95%) have been obtained by the proposed method. Experimental results demonstrate that our proposed approach is feasible and effective for dorsal hand vein recognition.

A secure palm vein recognition system

With the increasing needs in security systems, vein recognition is one of the important and reliable solutions of identity security for biometrics-based identification systems. The obvious and stable line-feature-based approach can be used to clearly describe a palm vein patterns for personal identification. In this paper, a directional filter bank involving different orientations is designed to extract the vein pattern and the minimum directional code (MDC) is employed to encode the line-based vein features in binary code. In addition, there are many non vein pixels in the vein image and those pixels are unmeaning for vein recognition. To improve the accuracy, the non-vein pixels are detected by evaluating the directional filtering magnitude (DFM) and considered the non-orientation code. A total of 5120 palm vein images from 256 persons are used to verify the validity of the proposed palm vein recognition approach. High accuracies (>99%) and low equal error rate (0.54%) obtained by the proposed method show that our proposed approach is feasible and effective for palm vein recognition.

Image Enhancement using Hybrid Fuzzy Inference System (IEHFS)

Image enhancement is a primary need for the recognition of different biometrics in biometric-based identification systems. The recognition-rate of a biometric system depends heavily upon the quality of the input biometric given to the system. In this paper, a novel hybrid Fuzzy model (IEHFS) is proposed to improve the visual quality of iris images. The experimental results based on calculating PSNR values show that this hybrid model enhances the noisy biometric images better than the conventional filters like median filter and Wiener filter.

Classifier ensembles and optimization techniques to improve the performance of cancellable fingerprint

The main aim of biometric-based identification systems is to automatically recognize individuals based on their physiological and/or behavioural characteristics such as fingerprint, face, hand-geometry, among others. These systems offer several advantages over traditional forms of identity protection. However, there are still some important aspects that need to be addressed in these systems. The main questions are concerned with the security of biometric authentication systems since it is important to ensure the integrity and public acceptance of these systems. In order to avoid the problems arising from compromised biometric templates, the concept of cancellable biometrics has recently been introduced. The concept is to transform a biometric trait into a new representation for enrolment and matching. Although cancellable biometrics were proposed to solve privacy concerns, the concept raises new issues, since they make the authentication problem more complex and difficult to solve. Thus, more effective authentication structures are needed to perform these tasks. In this paper, we investigate the use of ensemble systems in cancellable biometrics, using fingerprint-based identification to illustrate the possible benefits accruing. In order to increase the effectiveness of the proposed ensemble systems, three feature selection methods will be used to distribute the attributes among the individual classifiers of an ensemble. The main aim of this paper is to analyse the performance of such well-established structures on transformed biometric data to determine whether they have a positive effect on the performance of this complex and difficult task.

A novel approach to image quality assessment in iris recognition systems

With increasing needs in security systems, iris recognition is an important technique as one of the most reliable solutions for biometrics-based identification systems. However, not all of the iris images acquired from the device are in-focus and sharp enough for recognition. Thus, the poor quality of iris images has serious influence on the accuracy of iris recognition. Sometimes these images are not good enough due to a variety of factors: defocus blur, motion blur, eyelid occlusion and eyelash occlusion. This paper presents an approach for quality assessment of iris images, which can select the high quality iris images from the image sequences to be used in iris recognition systems. First, the gradient information of the iris regions (64 × 64) adjoining the pupil on the right and left sides is calculated to distinguish the blurred images from the in-focus images. Next, the valid iris regions are employed to discriminate between the occluded images and useful images. We present underlying theory as well as experimental results from both the CASIA iris database and the database provided for the iris challenge evaluation (ICE). The results show that this evaluation approach can actually reflect the real quality of iris images and significantly improve the overall performance of the iris recognition systems.

Analyzing the Benefits of a Novel Multiagent Approach in a Multimodal Biometrics Identification Task

Many approaches to the implementation of biometrics-based identification systems are possible, and different configurations are likely to generate significantly different operational characteristics. The choice of implementational structure is therefore very dependent on the performance criteria which are most important in any particular task scenario. In this paper we evaluate the merits of using multimodal structures, and we investigate how fundamentally different strategies for implementation can increase the degree of choice available in achieving particular performance criteria. In particular, we illustrate the merits of an implementation based on a multiagent computational architecture as a means of achieving high performance levels when recognition accuracy is a principal criterion. We also set out the relative merits of this strategy in comparison with other commonly adopted approaches to practical system realization. In particular we propose and evaluate a novel approach to implementation of a multimodal system based on negotiating agents.

Iris recognition with an improved empirical mode decomposition method

With the increasing need for security systems, iris recognition is one of the reliable solutions for biometrics-based identification systems. In general, an iris recognition algorithm includes four basic modules: image quality assessment, preprocessing, feature extraction, and matching. This work presents a whole iris recognition system, but particularly focuses on the image quality assessment and proposes an iris recognition scheme with an improved empirical mode decomposition (EMD) method. First, we assess the quality of each image in the input sequence and select clear enough iris images for the succeeding recognition processes. Then, an improved EMD (IEMD), a multiresolution decomposition technique, is applied to the iris pattern extraction. To verify the efficacy of the proposed approach, experiments are conducted on the public and freely available iris images from the CASIA and UBIRIS databases; three different similarity measures are used to evaluate the outcomes. The results show that the presented schemas achieve promising performance by those three measures. Therefore, the proposed method is feasible for iris recognition and IEMD is suitable for iris feature extraction.

Iris recognition using local texture analysis

With the increasing needs in security systems, iris recognition is reliable as one of the important solutions for biometrics-based identification systems. This work presents an effective approach for iris recognition by analyzing iris patterns. To improve the rate of recognition, we divide the normalized iris image into several regions to keep the iris image away from several noise factors, such as eyelids, eyelashes, and motion blur. For feature extraction, the local edge pattern (LEP) operator is designed to capture local characteristics of the iris image to produce discriminating texture features in every region. A resulting 2D feature vector is mapped into a low-dimensional subspace using two dimension linear discriminant analysis (2DLDA), and then the minimum distance classifier (MDC) is adopted for recognition. Experiments on the public and freely available iris images taken from the CASIA (Institute of Automation, Chinese Academy of Sciences) and UBIRIS databases confirm the advantage of the proposed approach in terms of speed and accuracy.