Biometric Personal Identification Research Articles

БАГАТОРІВНЕВА СИСТЕМА РОЗПІЗНАВАННЯ ОБЛИЧЧЯ

The problem of biometric person identification based on component-based face recognition is considered. It is shown that the face recognition system can be represented as a hierarchically organized multilevel system in which an ensemble of local classifiers forms “soft” decisions about the belonging of images of individual components of the face to given classes. Then, based on the integration of these decisions, the formation of the final decision on whether the recognized face belongs to one of the given classes is realized. The problems of constructing a model of a local classifier, as well as choosing an integrator of intermediate solutions of local classifiers, are formulated and solved. Keywords: pattern recognition, multilevel recognition system, classifier ensemble, classifier combination rule, decision making.

Brainprint based on functional connectivity and asymmetry indices of brain regions: A case study of biometric person identification with non‐expensive electroencephalogram headsets

AbstractBrain‐computer interface applications for biometric person identification have increased their interest in recent years since they are potentially more secure and more difficult to counterfeit than traditional biometric techniques. However, it is necessary to consider how brain waves are acquired for this purpose, not only in terms of efficiency but also of practical comfort for the user and the affordability degree of the biosignal acquisition device so that their everyday application can become a realistic possibility. In this context, this paper presents the capabilities of using a non‐expensive wireless electroencephalogram (EEG) device to extract spectral‐related and functional connectivity information of brain activity. The proposed method achieved a sufficient biometric identification with two datasets of 13 and 109 subjects when comparing the performance of a sizeable classification algorithm set. In addition, a novel feature in EEG biometric identification, called asymmetry index, is introduced here. Furthermore, this is the first study in this field to consider the effect of the time‐lapse between different recording sessions on the system's behaviour when using a low‐cost EEG device with identification accuracy rates of up to 100%.

Signature Recognition for Banking System

Abstract: The use of signatures for personal identification and verification is very common. Verifying signatures is necessary for many documents, including legal transactions and bank checks. The authentication of several papers using signatures is an extremely challenging and time-consuming job. As a result, systems for biometric personal verification and identification that rely on measurable, distinctive physical traits (such as fingerprints, hand geometry, faces, iris scans, or DNA) or behavioural traits have experienced an accelerating expansion (gait, voice etc.). Since tokens, passwords, pins, and other commonly used identity verification techniques have a number of fatal flaws and can't meet security requirements, the research looks into a feature that is far more often used: signature verification. We discuss the methods for authenticating signatures. We classify and outline the many techniques put forth for verification of signatures

Lip Reading using Convolutional Neural Network

Lipreading is that the task of understanding speech by analysing the movement of lips. instead, it can be represented because the method of secret writing text from visual info generated by the speaker’s mouth movement. The task of perception depends conjointly on info provided by the context and data of the language. Lipreading, conjointly called visual speech recognition could be a difficult task for humans, particularly within the absence of context. many apparently identical lip movements will turn out totally different words, thus perception is associate inherently ambiguous drawback within the word level. Even skilled lipreaders come through low accuracy in word prediction for datasets with solely a couple of words. machine-driven perception has been a subject of interest for several years. A machine that may browse lip movement has nice utility in varied applications such as: machine-driven perception of speakers with broken vocal tracts, biometric person identification, multi-talker coincidental speech secret writing, silent-movie process and improvement of audio-visual speech recognition normally. Here we've used Convolutional Neural Network (CNN) from deep learning. The advancements in machine learning created machine-driven perception attainable. Also, we've used MIRACL-VC1 Dataset for our reference. The accuracy of our project came dead set as 76%. Key Words: convolutional neural network, deep learning, lip reading, converting speech to text, open CV, keras.

Comparison Of LBPH, Fisherface, and PCA For Facial Expression Recognition of Kindergarten Student

Face recognition is the biometric personal identification that gaining a lot of attention recently. An increasing need for fast and accurate face expression recognition systems. Facial expression recognition is a system used to identify what expression is displayed by someone. In general, research on facial expression recognition only focuses on adult facial expressions. The introduction of human facial expressions is one of the very fields of research important because it is a blend of feelings and computer applications such as interactions between humans and computers, compressing data, face animation and face image search from a video. This research process recognizes facial expressions for toddlers, precisely for kindergarten students. But before making this research system Comparing three methods namely PCA, Fisherface and LBPH by adopts our new database that contains the face of individuals with a variety of pose and expression. which will be used for facial expression recognition. Fisherface accuracy was obtained at 94%, LBPH 100%, and PCA 48.75%.

EEG Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications.

Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual recording of the brain's electrical activities, measured by placing electrodes (channels) in various scalp positions. However, traditional EEG-based systems lead to high complexity with many channels, and some channels have critical information for the identification system while others do not. Several studies have proposed a single objective to address the EEG channel for person identification. Unfortunately, these studies only focused on increasing the accuracy rate without balancing the accuracy and the total number of selected EEG channels. The novelty of this paper is to propose a multiobjective binary version of the cuckoo search algorithm (MOBCS-KNN) to find optimal EEG channel selections for person identification. The proposed method (MOBCS-KNN) used a weighted sum technique to implement a multiobjective approach. In addition, a KNN classifier for EEG-based biometric person identification is used. It is worth mentioning that this is the initial investigation of using a multiobjective technique with EEG channel selection problem. A standard EEG motor imagery dataset is used to evaluate the performance of the MOBCS-KNN. The experiments show that the MOBCS-KNN obtained accuracy of 93.86% using only 24 sensors with AR20 autoregressive coefficients. Another critical point is that the MOBCS-KNN finds channels not too close to each other to capture relevant information from all over the head. In conclusion, the MOBCS-KNN algorithm achieves the best results compared with metaheuristic algorithms. Finally, the recommended approach can draw future directions to be applied to different research areas.

Measuring Neuromuscular Electrophysiological Activities to Decode HD-sEMG Biometrics for Cross-Application Discrepant Personal Identification With Unknown Identities

Measuring the physical, physiological, behavioural or chemical characteristics of an individual as biometrics for personal identification has attracted increasing attentions in smart environment applications. Noncancelability and cross-application invariance are two flaws of traditional DNA, face and fingerprint-based biometrics, because users cannot volitionally change the biometric template. In this work, we acquired high-density surface electromyogram (HD-sEMG) signals encoded by gesture passwords as biometrics. The different sEMG patterns under different motor tasks allow users to enroll multiple accounts using sEMG under different hand gestures as biometrics. By simply changing to a new gesture password, users can cancel the original template once it is compromised. Even if impostors enter the correct gesture password, the individual differences of HD-sEMG as the second defense, can still achieve excellent performance. To improve the current state-of-the-art identification accuracy, we acquired 256-channel forearm HD-sEMG and decoded high-resolution neuromuscular information in temporal-spectral-spatial domain. We achieved a high identification accuracy of 99.85% on a 200-account (20 subjects × 10 accounts per subject) recognition task, with training and testing data acquired 3–25 days apart. Moreover, to address the concern of “unknown identities,” we applied an “authentication + identification” validation, achieving a high accuracy of 93.81% on a 200-account ((16 enrolled subjects + 4 unknown subjects) × 10 accounts per subject) task. Our work substantially improves the current state-of-the-art accuracy for cross-day sEMG biometric identification (improved from ~ 88% to > 99%, with a similar number of identified classes).

EEG Channel Selection for Person Identification Using Binary Grey Wolf Optimizer

Electroencephalogram signals (EEG) have provided biometric identification systems with great capabilities. Several studies have shown that EEG introduces unique and universal features besides specific strength against spoofing attacks. Essentially, EEG is a graphic recording of the brain’s electrical activity calculated by sensors (electrodes) on the scalp at different spots, but their best locations are uncertain. In this paper, the EEG channel selection problem is formulated as a binary optimization problem, where a binary version of the Grey Wolf Optimizer (BGWO) is used to find an optimal solution for such an NP-hard optimization problem. Further, a Support Vector Machine classifier with a Radial Basis Function kernel (SVM-RBF) is then considered for EEG-based biometric person identification. For feature extraction purposes, we examine three different auto-regressive coefficients. A standard EEG motor imagery dataset is employed to evaluate the proposed method, including four criteria: (i) Accuracy, (ii) F-Score, (iii) Recall, and (v) Specificity. In the experimental results, the proposed method (named BGWO-SVM) obtained 94.13% accuracy using only 23 sensors with 5 auto-regressive coefficients. Besides, BGWO-SVM finds electrodes not too close to each other to capture relevant information all over the head. As concluding remarks, BGWO-SVM achieved the best results concerning the number of selected channels and competitive classification accuracies against other meta-heuristics algorithms.

Modern methods of human identification using gait characteristics

Automatic systems for personal identification based on gait occupy an important place among biometric identification systems. The development of information technologies has enabled a very intensive development and application of these systems in criminal and security sciences. This paper presents the systems of biometric personal identification using gait characteristics based on input data collected using different techniques, algorithms for extracting and selecting characteristics, different classifiers in the classification process, including databases used to evaluate and compare the efficiency of the system.

Cancelable HD-sEMG-Based Biometrics for Cross-Application Discrepant Personal Identification.

With the soaring development of body sensor network (BSN)-based health informatics, information security in such medical devices has attracted increasing attention in recent years. Employing the biosignals acquired directly by the BSN as biometrics for personal identification is an effective approach. Noncancelability and cross-application invariance are two natural flaws of most traditional biometric modalities. Once the biometric template is exposed, it is compromised forever. Even worse, because the same biometrics may be employed as tokens for different accounts in multiple applications, the exposed template can be used to compromise other accounts. In this work, we propose a cancelable and cross-application discrepant biometric approach based on high-density surface electromyogram (HD-sEMG) for personal identification. We enrolled two accounts for each user. HD-sEMG signals from the right dorsal hand under isometric contractions of different finger muscles were employed as biometric tokens. Since isometric contraction, in contrast to dynamic contraction, requires no actual movement, the users' choice to login to different accounts is greatly protected against impostors. We realized a promising identification accuracy of 85.8% for 44 identities (22 subjects × 2 accounts) with training and testing data acquired 9 days apart. The high identification accuracy of different accounts for the same user demonstrates the promising cancelability and cross-application discrepancy of the proposed HD-sEMG-based biometrics. To the best of our knowledge, this is the first study to employ HD-sEMG in personal identification applications, with signal variation across days considered.

Feature-level fusion of major and minor dorsal finger knuckle patterns for person authentication

The identification of individuals by their finger dorsal patterns has become a very active area of research in recent years. In this paper, we present a multimodal biometric personal identification system that combines the information extracted from the finger dorsal surface image with the major and minor knuckle pattern regions. In particular, first the features are extracted from each single region by BSIF (binarized statistical image features) technique. Then, extracted information is fused at feature level. Fusion is followed by dimensionality reduction step using PCA (principal component analysis) + LDA (linear discriminant analysis) scheme in order to improve its discriminatory power. Finally, in the matching stage, the cosine Mahalanobis distance has been employed. Experiments were conducted on publicly available database for minor and major finger knuckle images, which was collected from 503 different subjects. Reported experimental results show that feature-level fusion leads to improved performance over single modality approaches, as well as over previously proposed methods in the literature.

An Extensive Study of Issues, Challenges and Achievements in Iris Recognition

In recent years biometric identification of persons has gained major importance in the world from its applications, such as border security, access control and forensic. Iris recognition is one of the most booming biometric modalities. Due to its unique character as a biometric feature, iris identification and verification systems have become one of the most accurate biometric modality. In this paper, the different steps to recognize an iris image which includes acquisition, segmentation, normalization, feature extraction and matching are discussed. The performance of the iris recognition system depends on segmentation and normalization techniques adopted before extracting the iris features. It also provides an extensive review of the significant methods of iris recognition systems. In addition to this, the challenges and achievements of the iris are presented.

Speech Features Analysis and Biometric Person Identification in Multilingual Environment

IJSRNSC is a Non-Profit Organization; The IJSRNSC is dedicated to improvement in academic sectors of Science (Chemistry, Bio-chemistry, Zoology, Botany, Biotechnology, Pharmaceutical Science, Bioscience, Bioinformatics, Biometrics, Biostatistics, Microbiology, Environmental Management, Medical Science, Forensic Science, Home Science, Library Science, Material Science Military Science, Physical Science, Physical Education Science, Educational Science, Fisheries, seed technology, Agriculture, Forestry Science, Mathematics, Physics, Statistics and Geology/Earth Science), Computer Science, Engineering and Information Technology, Commerce, Management, Economics Sociology and Social Science.

An Investigation on the Accuracy of Truncated DKLT Representation for Speaker Identification With Short Sequences of Speech Frames.

Speaker identification plays a crucial role in biometric person identification as systems based on human speech are increasingly used for the recognition of people. Mel frequency cepstral coefficients (MFCCs) have been widely adopted for decades in speech processing to capture the speech-specific characteristics with a reduced dimensionality. However, although their ability to decorrelate the vocal source and the vocal tract filter make them suitable for speech recognition, they greatly mitigate the speaker variability, a specific characteristic that distinguishes different speakers. This paper presents a theoretical framework and an experimental evaluation showing that reducing the dimension of features by applying the discrete Karhunen-Loève transform (DKLT) to the log-spectrum of the speech signal guarantees better performance compared to conventional MFCC features. In particular with short sequences of speech frames, with typical duration of less than 2 s, the performance of truncated DKLT representation achieved for the identification of five speakers are always better than those achieved with the MFCCs for the experiments we performed. Additionally, the framework was tested on up to 100 TIMIT speakers with sequences of less than 3.5 s showing very good recognition capabilities.

ECG biometric recognition using SVM‐based approach

This paper presents a new approach for biometric personal identification based on electrocardiogram (ECG) features. ECG, which reflects cardiac electrical activity, is a distinctive characteristic of a person and can be used for security needs. Twenty‐one features based on temporal and amplitude distances between detected fiducial points and 10 morphological descriptors are extracted from each heartbeat. Then, support vector machine (SVM) is used as a classifier. A comparative study between two kernels, Gaussian and polynomial, was made in order to determine the best kernel and the appropriate values of hyperparameters that improve the recognition performance. The algorithm is evaluated using two databases, namely MIT‐BIH Arrhythmia and MIT‐BIH Normal Sinus Rhythm. Analysis of the results shows that the combination of all features allows improvement of our system efficiency with regard to healthy human subjects and those with arrhythmia. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Automatic correction of barrel distorted images using a cascaded evolutionary estimator

All optical systems are to some extent burdened by one or more aberrations. Barrel distortion of an image is also an aberration. In this paper we used an innovative method to solve the problem of the centric radial distortion of a static image which serves for biometric identification of persons using 2D contour of a human hand. The method proposed uses a cascaded arrangement of two algorithms – the classic meta-heuristic, referred to as “jDE-differential evolution” and an algorithm called Covariance Matrix Adaptation Evolution Strategy. Optimizers use methods of inverse engineering and numerical mathematics to resolve the question of how to determine the correct parameters of the algebraic polynomial equation of the nth degree, by the application of which it is possible to obtain an image free of barrel distortion from an image affected by this distortion. The proposed method provides a high-quality and time-acceptable method of optimization and the option of choosing the approximation accuracy. With the use of the coefficients obtained, it is then possible to use a method called back-mapping to permanently correct the centric radial distortion aberration in the biometric scanner. Extensive experiments presented in this paper enable a better understanding of relationships, the accuracy obtained, and options of using evolutionary optimizers in a larger sense.

Real-time NIR imaging of Palm Dorsa subcutaneous vein pattern based biometrics: An SRC based approach

With the advancement in biometric recognition techniques, Palm Dorsa Subcutaneous Vein Pattern (PDSVP) [1] has emerged as a reliable and promising physiological biometric feature. Consequently, PDSVP has been extensively used as a biometric feature in recent research works on biometrics. However, the procedure of automatic data acquisition and PDSVP extraction is quite challenging. This article shows how this problem can be addressed in practice where a real life biometric identification system has been developed utilising near infrared (NIR) imaging of PDSVP of humans, where no fixed setup is employed for data acquisition. We highlight, in this article, three key ideas: automatic data acquisition, PDSVP extraction and, subsequently, biometric person identification using sparse representation based classification. Finally, we highlight the research challenges associated with implementing a Biometric Identification System (BIS) using PDSVP as a physiological feature.

Mathematical and information maintenance of biometric systems

This article describes the different mathematical methods for processing biometric data. A brief overview of methods for personality recognition by means of a signature is conducted. Mathematical solutions of a dynamic authentication method are considered. Recommendations on use of certain mathematical methods, depending on specific tasks, are provided. Based on the conducted analysis of software and the choice made in favor of the wavelet analysis, a brief basis for its use in the course of software development for biometric personal identification is given for the purpose of its practical application.

Biometric Person Identification System: A Multimodal Approach Employing Spectral Graph Characteristics of Hand Geometry and Palmprint

Biometric authentication systems operating in real world environments using a single modality are found to be insecure and unreliable due to numerous limitations. Multimodal biometric systems have better accuracy and reliability due to the use of multiple biometric traits to authenticate a claimed identity or perform identification. In this paper a novel method for person identification using multimodal biometrics with hand geometry and palmprint biometric traits is proposed. The geometrical information embedded in the user hand and palmprint images are brought out through the graph representations. The topological characterization of the image moments, represented as the virtual nodes of the palmprint image graph is a novel feature of this work. The user hand and palmprint images are represented as weighted undirected graphs and spectral characteristics of the graphs are extracted as features vectors. The feature vectors of the hand geometry and palmprint are fused at feature level to obtain a graph spectral feature vector to represent the person. User identification is performed by using a multiclass support vector machine (SVM) classifier. The experimental results demonstrate, an appreciable performance giving identification rate of 99.19% for multimodal biometric after feature level fusion of hand geometry and palmprint modalities. The performance is investigated by conducting the experiments separately for handgeometry, palmprint and fused feature vectors for person identification. Experimental results show that the proposed multimodal system achieves better performance than the unimodal cues, and can be used in high security applications. Further comparison show that it is better than similar other multimodal techniques.

A Basic Study of Biometric Personal Identification Technique on Smartphone Using Key Stroke

A Basic Study of Biometric Personal Identification Technique on Smartphone Using Key Stroke