Person Authentication System Research Articles

ENHANCE DOCUMENT VALIDATION UIPATH POWERED SIGNATURE VERIFICATION

Abstract—Signatures are widely used as a means of personal identification and verification. Many documents like bank cheques and legal transactions require signature verification. Signature-based verification of a large number of documents is a very difficult and time-consuming task. Consequently, an explosive growth has been observed in biometric personal verification and authentication systems that are connected with quantifiable physical unique characteristics (finger prints, hand geometry, face, ear, iris scan, or DNA) or behavioural features (gait, voice etc.). As traditional identity verification methods such as tokens, passwords, pins etc suffer from some fatal flaws and are incapable to satisfy the security necessities, the paper aims to consider a more reliable biometric feature, signature verification for the considering. We present a survey of signature verification systems. We classify and give an account of the various approaches that have been proposed for signature verification.

Personal authentication system utilizing voice distortion through guitar effectors

In this study, we propose a personal authentication system using processed speech that can only be understood by those who know the original voice. The proposed system presents non-invertible processed speech signals, including distortion, equalization, and echo, to the voice representing the words registered by the legitimate user at the time of authentication, just like a signal processed through guitar effects pedals. The strength of the effect is set within the range where the legitimate user can barely be identified, and the combination of effect parameters can be changed each time it is presented, making it harder for everyone other than the legitimate user to break through. We confirmed the system's effectiveness by utilizing a dataset of Japanese words with defined word familiarity. Through an authentication experiment in which the system was automatically adjusted to an effect strength that was barely recognizable to legitimate users, it was found that legitimate users could identify 75.6% of the processed speech signals. In comparison, non-legitimate users could decode only 38.8% of the words. These results indicate the feasibility of a personal authentication system utilizing distorted speech signals.

A novel intelligent 12-layer convolutional neural network model for gender classification using fingerprint images

Fingerprints are widely used as effective personal authentication systems, because they constitute unique, robust, and risk-free evidence. Fingerprinting techniques refer to biometric procedures used for identifying individuals based on their physical characteristics. A fingerprint image contains ridges and valleys forming a directionally-oriented pattern. The robustness of the fingerprint authentication technique determines the quality of the fingerprint image. This study proposed an intelligent 12-layered Convolutional Neural Network (CNN) model using Deep learning (DL) for gender determination based on fingerprints. Further, the study compared the performance of this model to existing state-of-the-art methods. The primary goal of this study was to reduce the number of comparisons within a large database obtained from automatic fingerprint recognition systems. The classification process was found to be swifter and more accurate when analysis of the DL algorithm was performed. With reference to the criteria of precision, recall, and accuracy evaluation during classification, this proposed 12-layered CNN model outperformed the Residual Neural Network with 50 Layers (ResNet-50) and Dense Convolutional Network with 201 Layers (DenseNet-201) models. The accuracies obtained were 97.0%, 95.8%, 98.0%, and 96.8% for female-left, female-right, male-left, and male-right classes respectively, while achieving an overall accuracy of 94.0%.

Personal Authentication System Based Iris Recognition withDigital Signature Technology

Authentication based on biometrics isused to prevent physical access to high-security institutions. Due to the rapid rise of information system technologies, Biometrics is now being used in applications for accessing databases and commercial workflowsystems. These applications need to implement measures to counter security threats. Many developers are exploring and developing novel authentication techniques to prevent these attacks. However, the most challenging problem is keeping biometric data while maintaining the functional performance ofidentity verification systems. This paper presents a biometrics-based personal authentication system combining a smart card, a Public Key Infrastructure (PKI), and iris verification technologies. Raspberry Pi 4 Model B+ is the core of hardware componentswith an IR Camera. Following that idea, we designed an optimal image processing algorithm in OpenCV/ Python, Keras, and sci-kit learn libraries for feature extraction and recognition chosen for application development in this project. The implemented systemgives an accuracy of (97% and 100%) for the left and right (NTU) iris datasets, respectively, after training. Later, the person verification based on the iris feature is performed to verify the claimed identity and examine the system authentication. Thetime of essential generation, Signature, and Verification is 5.17sec,0.288, and 0.056 for the NTU iris dataset. This work offers a realistic architecture to implement identity-based cryptography with biometrics using the RSA algorithm.

RGB-D Video based Hand Authentication using Deep Neural Network

In recent years, behavioral biometrics authentication, which uses the habit of behavioral characteristics for personal authentication, has attracted attention as an authentication method with higher security since behavioral biometrics cannot mimic fingerprint and face authentications. In behavioral biometrics, extensive research has been done on voiceprints and gait. However, there are few authentication technologies that utilize the habits of hand and finger movements during hand gestures. Only color images or depth images are used for hand gesture authentication in conventional methods. In this research, therefore, we propose to find individual habits from RGB-D images of finger movements and create a personal authentication system. 3D CNN, which is deep learning-based network, is used to extract individual habits. F-measure of 0.97 is achieved when rock-paper-scissors are used as the authentication operation. F-measure of 0.97 is achieved when the disinfection operation is used. These results show the effectiveness of using RGB-D video for personal authentication.

GAN-based patient information hiding for an ECG authentication system.

Various biometrics such as the face, irises, and fingerprints, which can be obtained in a relatively simple way in modern society, are used in personal authentication systems to identify individuals. These biometric data are extracted from an individual's physiological data and yield high performance in identifying an individual using unique data patterns. Biometric identification is also used in portable devices such as mobile devices because it is more secure than cryptographic token-based authentication methods. However, physiological data could include personal health information such as arrhythmia related patterns in electrocardiogram (ECG) signals. To protect sensitive health information from hackers, the biomarkers of certain diseases or disorders that exist in ECG signals need to be hidden. Additionally, to implement the inference models for both arrhythmia detection and personal authentication in a mobile device, a lightweight model such as a multi-task deep learning model should be considered. This study demonstrates a multi-task neural network model that simultaneously identifies an individual's ECG and arrhythmia patterns using a small network. Finally, the computational efficiency and model size of the single-task and multi-task models were compared based on the number of parameters. Although the multi-task model has 20,000 fewer parameters than the single-task model, they yielded similar performance, which demonstrates the efficient structure of the multi-task model.

Identity Verification using Geometry of Human Hands

Biometrics which can be used for identification of individuals based on their physical or behavioural characteristics has gained importance in today’s society where information security is essential. Hand geometry based biometrics systems are gaining acceptance in low to medium security applications. Hand geometry based identification systems utilize the geometric features of the hand like length and width of the fingers, diameter of the palm and the perimeter. The proposed system is a verification system which utilizes these hand geometry features for user authentication. This project introduces an inexpensive, powerful and easy to use hand geometry based biometric person authentication system. One of the novelties of this work comprises on the introduction of hand geometry’s related, position independent, feature extraction and identification which can be useful in problems related to image processing and pattern recognition. Today students’ (class) attendance became more important part for any organizations/institutions. The conventional method of taking attendance by calling names or signing on paper is very time consuming and insecure, hence inefficient. This paper presents the manual students’ attendance management into computerized system for convenience or data reliability. So, the system is developed by the integration of ubiquitous computing systems into classroom for managing the students’ attendance using palm print scanner. The system is designed to implement an attendance management system based on palm print scanner which students need to use their palm to success the attendance where only authentic student can be recorded the attendance during the class. This system takes attendance electronically with the help of the webcam, and the records the attendance in a database. Students’ roll call percentages and their details are easily seen via Graphical User Interface (GUI).

Intelligent Feature Selection for ECG-Based Personal Authentication Using Deep Reinforcement Learning.

In this study, the optimal features of electrocardiogram (ECG) signals were investigated for the implementation of a personal authentication system using a reinforcement learning (RL) algorithm. ECG signals were recorded from 11 subjects for 6 days. Consecutive 5-day datasets (from the 1st to the 5th day) were trained, and the 6th dataset was tested. To search for the optimal features of ECG for the authentication problem, RL was utilized as an optimizer, and its internal model was designed based on deep learning structures. In addition, the deep learning architecture in RL was automatically constructed based on an optimization approach called Bayesian optimization hyperband. The experimental results demonstrate that the feature selection process is essential to improve the authentication performance with fewer features to implement an efficient system in terms of computation power and energy consumption for a wearable device intended to be used as an authentication system. Support vector machines in conjunction with the optimized RL algorithm yielded accuracy outcomes using fewer features that were approximately 5%, 3.6%, and 2.6% higher than those associated with information gain (IG), ReliefF, and pure reinforcement learning structures, respectively. Additionally, the optimized RL yielded mostly lower equal error rate (EER) values than the other feature selection algorithms, with fewer selected features.

“Gesture Identifier”

Abstract: In modern society, where information security is emphasized, the importance of biometrics, which identifies individuals based on their physical and behavioral characteristics, is increasing. Hand geometry based biometric systems are gaining acceptance in low-to-medium security applications. Hand geometry -based identification systems use geometric characteristics of the hand, such as finger length and width, palm diameter and circumference. The proposed system is an authentication system that uses these hand shape features for user authentication. This project presents a low-cost, high-performance, and easy-to-use hand-geometry based biometric personal authentication system. One of the novelties of this work involves the introduction of position-based, position-independent feature extraction and hand shape identification, useful for problems related to image processing and pattern recognition. Today, the presence of students (classes) has become an important part of every organization/institution. Traditional methods of recording attendance through paper attribution or signatures are very time consuming, insecure and inefficient. This paper describes manual student attendance management in a computerized system for convenience or data reliability. Therefore, the system was developed by integrating a ubiquitous computing system into the classroom to manage student attendance with palmprint scanners

Personal Authentication System Based on Iris Recognition and Digital Signature Technology

Authentication based on biometrics is being used to prevent physical access to high-security institutions. Recently, due to the rapid rise of information system technologies, biometrics is now being used in applications to access databases and commercial workflow systems. These applications need to implement measures to counter security threats. Many developers are exploring and developing novel authentication techniques to prevent these attacks. However, the most challenging problem is how to keep biometric data while maintaining the functional performance of identity verification systems. This paper presents a biometrics-based personal authentication system in which a smart card, a Public Key Infrastructure (PKI), and iris verification technologies are combined. Raspberry Pi 4 Model B+ is used as the core of hardware components with an IR Camera. Following that idea, we designed an optimal image processing algorithm in OpenCV, Python, Keras, and sci-kit learn libraries for feature extraction and recognition is chosen for application development in this project. After training, the implemented system gives an accuracy of (97% and 100%) for the left and right (NTU) iris datasets. Later, the person verification based on the iris feature is performed to verify the claimed identity and examine the system authentication. For the NTU iris dataset, the time of key generation, Signature, and Verification is 5.17sec,0.288, and 0.056, respectively. This work offers the realistic architecture to implement identity-based cryptography with biometrics using the Rivest–Shamir–Adleman (RSA) algorithm.

Retinal blood vessel segmentation using multiple line operator-based methods

The morphological alterations of the retinal blood vessels are important indicators that can be utilized to diagnose and track the progression of a number of disorders. Diabetic retinopathy (DR) is a condition that destroys the retina and is the major cause of visual loss caused by high blood glucose levels. One of the retinal objects impacted by DR is the blood vessel. By regularly monitoring changes in the retinal blood vessels, severe DR or even vision loss can be avoided. The condition of the blood vessel can be examined by segmenting the blood vessel area from a digital fundus image. Segmenting retinal blood vessels manually, on the other hand, is time-consuming and tedious, and especially when dealing with a high number of photographs. As a result, a system for segmenting retinal blood vessels automatically is crucial. Furthermore, methods for automatically segmenting retinal blood vessels are useful for person authentication systems based on the retina. Blood vessel segmentation can be accomplished in a number of ways. Based on the prior line operator method, an improved version of the line operator method is proposed in this paper. The proposed method demonstrates an improvement in accuracy over the previous method, with an accuracy of 94.61%.

A Fingerprint-Based Verification Framework Using Harris and SURF Feature Detection Algorithms

Amongst all biometric-based personal authentication systems, a fingerprint that gives each person a unique identity is the most commonly used parameter for personal identification. In this paper, we present an automatic fingerprint-based authentication framework by means of fingerprint enhancement, feature extraction, and matching techniques. Initially, a variant of adaptive histogram equalization called CLAHE (contrast limited adaptive histogram equalization) along with a combination of FFT (fast Fourier transform), and Gabor filters are applied to enhance the contrast of fingerprint images. The fingerprint is then authenticated by picking a small amount of information from some local interest points called minutiae point features. These features are extracted from the thinned binary fingerprint image with a hybrid combination of Harris and SURF feature detectors to render significantly improved detection results. For fingerprint matching, the Euclidean distance between the corresponding Harris-SURF feature vectors of two feature points is used as a feature matching similarity measure of two fingerprint images. Moreover, an iterative algorithm called RANSAC (RANdom SAmple Consensus) is applied for fine matching and to automatically eliminate false matches and incorrect match points. Quantitative experimental results achieved on FVC2002 DB1 and FVC2000 DB1 public domain fingerprint databases demonstrate the good performance and feasibility of the proposed framework in terms of achieving average recognition rates of 95% and 92.5% for FVC2002 DB1 and FVC2000 DB1 databases, respectively.

Hand authentication from RGB-D video based on deep neural network

In recent years, behavioral biometrics authentication, which uses the habit of behavioral characteristics for personal authentication, has attracted attention as an authentication method with higher security since behavioral biometrics cannot mimic as fingerprint and face authentications. As the behavioral biometrics, many researches were performed on voiceprints. However, there are few authentication technologies that utilize the habits of hand and finger movements during hand gestures. Only either color images or depth images are used for hand gesture authentication in the conventional methods. In the research, therefore, we propose to find individual habits from RGB-D images of finger movements and create a personal authentication system. 3D CNN, which is a deep learning-based network, is used to extract individual habits. An F-measure of 0.97 is achieved when rock-paper-scissors are used as the authentication operation. An F-measure of 0.97 is achieved when the disinfection operation is used. These results show the effectiveness of using RGB-D video for personal authentication.

Finger knuckle pattern person authentication system based on monogenic and LPQ features

Finger knuckle pattern person authentication system based on monogenic and LPQ features

Speaker Recognition System Based on Mel Frequency Cepstral Coefficient and Four Features

Biometrics signs are the most important factor in the human recognition field and considered an effective technique for person authentication systems. Voice recognition is a popular method to use due to its ease of implementation and acceptable effectiveness. This research paper will introduce a speaker recognition system that consists of preprocessing techniques to eliminate noise and make the sound smoother. For the feature extraction stage, the method Mel Frequency Cepstral Coefficient (MFCC) is used, and in the second step, the four features (FF) Mean, Standard Division, Zero-Cross and Amplitude, which added to (MFCC) to improve the results. For data representation, vector quantization has been used. The evaluation method (k-fold cross-validation) has been used. Supervised machine learning (SML) is proposed using Quadratic Discriminant Analysis (QDA) classification algorithms. And the results obtained by the algorithm (QDA) varied between 98 percent and 98.43 percent, depending on the way of features extraction that was used. These results are satisfactory and reliable. Index Terms— SML, QDA, Voice Recognition, MFCC, FF.

다수의 얼굴 인식을 통한 자동출석체크 시스템

Non-contact biometric identification technology is once again receiving social attention these days when untact technology is required due to the Covid-19. Face recognition technology is being used in a variety of platforms such as airport immigration systems, hospital patient management systems, financial self-authentication systems, criminal suspect detection systems, and personal authentication systems for mobile devices, and is showing its effectiveness. In this paper, multiple face recognition technology is applied to the automatic attendance system. Unlike when only one person is recognized, multiple face recognition results in lower accuracy and slower processing speed. In this paper, a frontal face detection algorithm is proposed. Using the frontal face detection algorithm, only the frontal face was used for recognition, and when 36 people were recognized, 97.2% accuracy was achieved. In addition, we proposed an erasing multiple face recognition method and increased the average number of frames processed per second as the recognition completion rate increased.

Generic speech based person authentication system with genuine and spoofed utterances: different feature sets and models

Generic speech based person authentication system with genuine and spoofed utterances: different feature sets and models

Development of Miniaturized Wearable Wristband Type Surface EMG Measurement System for Biometric Authentication

Personal authentication systems employing biometrics are attracting increasing attention owing to their relatively high security compared to existing authentication systems. In this study, a wearable electromyogram (EMG) system that can be worn on the forearm was developed to detect EMG signals and, subsequently, apply them for personal authentication. In previous studies, wet electrodes were attached to the skin for measuring biosignals. Wet electrodes contain adhesives and conductive gels, leading to problems such as skin rash and signal-quality deterioration in long-term measurements. The miniaturized wearable EMG system developed in this study comprised flexible dry electrodes attached to the watch strap, enabling EMG measurements without additional electrodes. In addition, for accurately classifying and applying the measured signal to the personal authentication system, an optimal algorithm for classifying the EMG signals based on a multi-class support vector machine (SVM) model was implemented. The model using cubic SVM achieved the highest personal authentication rate of 87.1%. We confirmed the possibility of implementing a wearable authentication system by measuring the EMG signal and artificial intelligence analysis algorithm presented in this study.

Wavelet Region implanting watermark upgrades the security framework in Digital Speech Watermarking

The progressions in innovation helped us to use biometric characteristics to verify the people without having them physically. To limit unapproved people and to encourage approved people different human characteristics are used. Face, unique marks, retina, iris, and DNA are some of the biometrics which is used most often to identify people. For additional security in-person authentication and identification systems, a combination of this biometrics can be utilized. Voice can also be used as a biometric similar to other biometrics. Special equipment and computer systems are required to separate this biometrics. This work helps to enhance security by upgrading the modules and embedding the watermark in the speech signal, in-person verification systems. Speakers are validated based on the watermark present in the speech signal. Energy calculations are performed for detail coefficients to select the coefficients where the watermark has to be implanted. The coefficients with less energy are selected for watermark embedding. Inverse discrete transform is applied on approximation and detail coefficients to produce the watermarked speech. The exhibition of the work is assessed by utilizing the subjective and objective measurements such as peak signal to noise ratio, bit error rate, and perceptual evaluation of speech quality. Peak signal to noise ratio is calculated between unique watermark and separated watermark and, unique and watermarked speech. An arrangement of speech articulations of the speaker during the training stage is used to develop the models for the person authentication system. During testing, structures are removed after original and watermarked speech articulations and are used towards calculate the correctness of the system.

Jointly learning compact multi-view hash codes for few-shot FKP recognition

As a relatively new biometric trait, Finger-Knuckle-Print (FKP) plays a vital role in establishing a personal authentication system in modern society due to its rich discriminative features, low time cost in image capture and user-friendliness. However, most existing KFP descriptors are hand-crafted and fail to work well with limited training samples. In this paper, we propose a feature learning method for few-shot FKP recognition by jointly learning compact multi-view hash codes (JLCMHC) of a FKP image. We first form the multi-view data vectors (MVDV) to exploit the multiple feature-specific information from a FKP image. Then, we learn a feature projection to encode the MVDV into compact binary codes in an unsupervised manner, where 1) the variance of the learned feature codes on each view is maximized and 2) the difference of the inter-view binary codes is enlarged, so that the redundant information in MVDV is reduced and more informative features can be obtained. Lastly, we pool the binary codes into block-wise statistics features as the final descriptor for FKP representation and recognition. Experimental results on the existing benchmark FKP databases clearly show that the JLCMHC method outperforms the state-of-the-art FKP descriptors.