Personal Authentication Research Articles

Jointly Heterogeneous Palmprint Discriminant Feature Learning.

Heterogeneous palmprint recognition has attracted considerable research attention in recent years because it has the potential to greatly improve the recognition performance for personal authentication. In this article, we propose a simultaneous heterogeneous palmprint feature learning and encoding method for heterogeneous palmprint recognition. Unlike existing hand-crafted palmprint descriptors that usually extract features from raw pixels and require strong prior knowledge to design them, the proposed method automatically learns the discriminant binary codes from the informative direction convolution difference vectors of palmprint images. Differing from most heterogeneous palmprint descriptors that individually extract palmprint features from each modality, our method jointly learns the discriminant features from heterogeneous palmprint images so that the specific discriminant properties of different modalities can be better exploited. Furthermore, we present a general heterogeneous palmprint discriminative feature learning model to make the proposed method suitable for multiple heterogeneous palmprint recognition. Experimental results on the widely used PolyU multispectral palmprint database clearly demonstrate the effectiveness of the proposed method.

Finger knuckle print recognition for personal authentication based on relaxed local ternary pattern in an effective learning framework

Finger knuckle print (FKP) as a physiological trait with a small image dimension, also a highly distinctive pattern, can be used as a reliable biometric identifier. In this paper, a new effective biometric authentication system using FKP texture based on relaxed local ternary pattern (RLTP) is presented. To further improve performance, cascading, overlapped patching and uniform rotation invariant pattern selection are used. Also to obtain more discriminative dominant patterns, an efficient learning framework is integrated with RLTP feature vectors. Identification and verification experiments conducted on the standard PolyU FKP dataset show the effectiveness of the proposed scheme.

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.

Toward Personal Authentication Using Gesture Classification with EMG Data

Toward Personal Authentication Using Gesture Classification with EMG Data

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.

A novel approach for designing authentication system using a picture based P300 speller.

Due to great advances in the field of information technology, the need for a more reliable authentication system has been growing rapidly for protecting the individual or organizational assets from online frauds. In the past, many authentication techniques have been proposed like password and tokens but these techniques suffer from many shortcomings such as offline attacks (guessing) and theft. To overcome these shortcomings, in this paper brain signal based authentication system is proposed. A Brain-Computer Interface (BCI) is a tool that provides direct human-computer interaction by analyzing brain signals. In this study, a person authentication approach that can effectively recognize users by generating unique brain signal features in response to pictures of different objects is presented. This study focuses on a P300 BCI for authentication system design. Also, three classifiers were tested: Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor, and Quadratic Support Vector Machine. The results showed that the proposed visual stimuli with pictures as selection attributes obtained significantly higher classification accuracies (97%) and information transfer rates (37.14 bits/min) as compared to the conventional paradigm. The best performance was observed with the QDA as compare to other classifiers. This method is advantageous for developing brain signal based authentication application as it cannot be forged (like Shoulder surfing) and can still be used for disabled users with a brain in good running condition. The results show that reduced matrix size and modified visual stimulus typically affects the accuracy and communication speed of P300 BCI performance.

Feature extraction of finger-vein patterns based on boosting evolutionary algorithm and its application for loT identity and access management

With billions of devices being connected, how to make sure that our information stays secure is becoming a hot topic of IoT. Traditional approaches to personal authentication are inadequate and ineffective in the IoT era. Finger vein technology is the current biometric system that utilizes the vein structure for recognition. As such patterns are veiled under the skin surface, they have significant privacy protection and are therefore incredibly difficult to forge. Finger vein recognition has gained a great deal of publicity because earlier approaches experienced significant pitfalls, such as its inability to handle the imbalanced collection of finger veins samples and the detection of distinguishing features in low-quality images. Such disadvantages have triggered a lack of consistency of the optimization algorithm or have contributed to a decrease in its efficiency. The key objective of the research discussed in this paper is to examine the impact of the genetic algorithm in the selection of the optimum vector characteristics of the finger vein. This is done by incorporating a Niching model in the form of a Context-Based Clearing (CBC) procedure to increase the heterogeneity of the features within the features’ vector, with the goal of minimizing the association between them. It also offers the idea of a reduction of the feature set to reduce duplication without reducing accuracy. The performance study of the proposed model is carried out through multiple tests and the findings indicate an overall increase of 6% in the accuracy relative to some of the state-of-the-art finger vein recognition systems present in the literature.

Multilateral Personal Portfolio Authentication System Based on Hyperledger Fabric

Korean education-related evaluation agencies utilize a centralized system that directly manages learner data. This leaves the intellectual property of the organizations and the personal information of the students vulnerable to leakage should the central server be attacked. In this study, the researchers propose a multilateral personal portfolio authentication system that guarantees the reliability, integrity, and transparency of the data such as learner's schooling history. The system uses the features of blockchain in a distributed network wherein a learner submits schooling data to a peer in the network. The data is then verified through an agreement among the peers and recorded in a chronologically encrypted ledger. The proposed system is implemented based on Hyperledger Fabric and the analysis thereof conducted by evaluating its processing speed, capacity, and security. The analysis indicates that the system is able to successfully intercept the transactions of unvalidated users, thereby preventing the recording of incorrect data by an unauthorized user. Furthermore, it provides a record of previously made changes to a learner's profile, thus improving the integrity and reliability of the data. This system provides a platform to share learner information safely and promptly among schools, certification authorities, and higher learning institutions.

Personal Authentication Using Finger Vein Biometric Technology with Implementation of Transfer Learning CNN Model

Personal Authentication Using Finger Vein Biometric Technology with Implementation of Transfer Learning CNN Model

Impact of Globalisation on Data Security – Authentication Issues

Research background:In today’s globalised world, there is an increasing need for reliable verification of users’ identity accessing various types of information systems. This verification is realised through authentication, which is traditionally divided according to the kind of identification mark that is used: knowledge authentication (e.g., passwords, control questions), authentication through an authentication object (e.g., magnetic cards, smart cards) and biometric authentication (e.g., voice, face recognition). However, it should be noted that this identity may not only be the specific identity of the user but also, for example, his group affiliation or ability.Purpose of the article:This paper aims to identify and describe the impact of globalisation on selected types of authentication.Methods:As the representative of the current methods of authentication of persons are selected a password authentication and hand-based biometric authentication. The most often used methods of password attacks are simulated through mathematical modelling, and the results are compared concerning the timeline. Through modelling, multi characteristics authentication using the thermal characteristics of the hand will be presented.Findings & Value added:Through mathematical modelling, the article demonstrates the influence of globalisation on the resilience of passwords to the most commonly used attacks and discusses the impact of globalisation on the requirements for modern forms of authentication.

A hybrid multimodal biometric scheme based on face and both irises integration for person authentication

In the biometric field, mono-modal biometry suffers from multiple lacks. The use of multiple biometrics has helped getting over those limitations and has allowed outperforming single biometrics in terms of recognition rate. In this paper, we propose a new approach that fuses the Gabor-Winger transform and the oriented Gabor phase information for feature extraction as well as a hybrid scheme consisting of multi-algorithm, multi-instance and multi-modal systems that integrates face and both left and right irises of the same subject. The SDUMLAHMT database has been used to evaluate the proposed approach. The results show that our multi-modal biometric system achieves higher accuracy than the single biometric approaches as well as the other existing multi-biometric systems.

LB and LT feature approach to personal identification using finger knuckle image biological trait

Biometric identification is an emerging field for personal authentication and has a large number of applications in the field of time attendance system and forensic domain. A variety of biometric traits are available, but among them, hand-based biometrics are more popular because of their ease of use and better performance. A lot of literature is available on fingerprint identification but it is observed that fingerprints are always not a reliable source of information to be captured from the crime scene to identify suspects. Therefore, it is required to use some other hand-based biological trait such as finger knuckle print (back side of finger joint skin pattern) in order to identify the suspect. This paper proposed a finger knuckle image-based person identification. The performance of the proposed biometric system is compared with the well-established fingerprint-based authentication system. The experiments were performed on the benchmark dataset like PolyU finger knuckle print dataset and FVC2002 fingerprint dataset. The experimental results show that the performance of the proposed algorithm leads over many fingerprint-based identification systems. The proposed algorithm can also be used to design finger knuckle image-based biometric systems instead of fingerprint-based biometric systems.

Region of Interest Extraction for Personal Authentication Using Automated Thresholding

Region of Interest Extraction for Personal Authentication Using Automated Thresholding

Forge fingerprints: Fabrication, development, and detection techniques

Fingerprint verification is extensively used for personal authentication and is the most advanced existing tool in biometric applications. Personality traits such as facial features or signature of a person tend to change with aging, and these can be fabricated or imitated with new tools available in the Dark web. But fingerprints of any individual are supposed to be unique as these remain unchanged throughout life, except due to deep injuries on skin, genetic disorder adermatoglyphia and in old age due to skin loosening elasticity. The latent fingerprints are the most prominent category of fingerprints which used as evidence in most of the crime scene. With advancement of technology, use of fingerprints is being extensively used for the personalised authentication of important documents. The criminals too are reportedly improving their modus operandii to imitate/forge the fingerprints to get access to these documents. Large numbers of techniques to create artificial fingerprints and to identify such forgeries have been developed over the years. This review article is a brief of various techniques employed in fingerprint forgery and analysis techniques to detect fingerprint forgery which have been discussed in various research papers and news articles.

Deep Distillation Hashing for Unconstrained Palmprint Recognition

Deep palmprint recognition has become an emerging issue with great potential for personal authentication on handheld and wearable consumer devices. Previous studies of palmprint recognition are mainly based on constrained data sets collected by dedicated devices in controlled environments, which has to reduce the flexibility and convenience. In addition, general deep palmprint recognition algorithms are often too heavy to meet the real-time requirements of embedded system. In this article, a new palmprint benchmark is established, which consists of more than 20 000 images collected by five brands of smartphones in an unconstrained manner. Each image has been manually labeled with 14 key points for the region of interest (ROI) extraction. Furthermore, a novel deep distillation hashing (DDH) algorithm is proposed as a benchmark for efficient deep palmprint recognition. Palmprint images are converted to binary codes to improve the efficiency of feature matching. Derived from knowledge distillation, new distillation loss functions are constructed to compress the deep model to further improve the efficiency of feature extraction on the light network. Comprehensive experiments are conducted on both constrained and unconstrained palmprint databases. Using DDH, the accuracy of palmprint identification can be increased by up to 11.37%, and the equal error rate (EER) of palmprint verification can be reduced by up to 3.11%. The results indicate the potential of our database, and DDH can outperform other baselines to achieve the state-of-the-art performance. The collected data set is publicly available at http://gr.xjtu.edu.cn/web/bell/resource .

A hybrid multimodal biometric scheme based on face and both irises integration for person authentication

In the biometric field, mono-modal biometry suffers from multiple lacks. The use of multiple biometrics has helped getting over those limitations and has allowed outperforming single biometrics in terms of recognition rate. In this paper, we propose a new approach that fuses the Gabor-Winger transform and the oriented Gabor phase information for feature extraction as well as a hybrid scheme consisting of multi-algorithm, multi-instance and multi-modal systems that integrates face and both left and right irises of the same subject. The SDUMLAHMT database has been used to evaluate the proposed approach. The results show that our multi-modal biometric system achieves higher accuracy than the single biometric approaches as well as the other existing multi-biometric systems.

Contactless Multi-biometric System Using Fingerprint and Palmprint Selfies

Due to the COVID-19 pandemic, automated contactless person identification based on the human hand has become very vital and an appealing biometric trait. Since, people are expected to cover their faces with masks, and advised avoiding touching surfaces. It is well-known that usually contact-based hand biometrics suffer from issues like deformation due to uneven distribution of pressure or improper placement on sensor, and hygienic concerns. Whereas, to mitigate such problems, contactless imaging is expected to collect the hand biometrics information without any deformation and leading to higher person recognition accuracy; besides maintaining hygienic and pandemic concerns. Towards this aim, in this paper, an effective multi-biometric scheme for person authentication based on contactless fingerprint and palmprint selfies has been proposed. In this study, for simplicity and efficiency, three local descriptors, i.e., local phase quantization (LPQ), local Ternary patterns (LTP), and binarized statistical image features (BSIF), have been employed to extract salient features from contactless fingerprint and palmprint selfies. The score level fusion based multi-biometric system developed in this work combines the matching scores using two different fusion techniques, i.e., transformation based-rules like triangular norms and classifier based-rules like SVM. Experimental results on two publicly available databases (i.e., PolyU contactless to contact-based fingerprint database and IIT-Delhi touchless palmprint dataset) show that the proposed contactless multi-biometric selfie system can easily outperform uni-biometrics.

Multimodal Biometric-Based Authentication with Secured Templates

This paper proposes a multimodal biometric based authentication (verification and identification) with secured templates. Multimodal biometric systems provide improved authentication rate over unimodal systems at the cost of increased concern for memory requirement and template security. The proposed framework performs person authentication using face and fingerprint. Biometric templates are protected by hiding fingerprint into face at secret locations, through blind and key-based watermarking. Face features are extracted from approximation sub-band of Discrete Wavelet Transform, which reduces the overall working plane. The proposed method also shows high robustness of biometric templates against common channel attacks. Verification and identification performances are evaluated using two chimeric and one real multimodal dataset. The same systems, working with compressed templates provides considerable reduction in overall memory requirement with negligible loss of authentication accuracies. Thus, the proposed framework offers positive balance between authentication performance, template robustness and memory resource utilization.

Palm Print Features for Personal Authentication Based On Seven Moments

Palm Print Features for Personal Authentication Based On Seven Moments

Retracted: Innovative approach for multimodal fusion recognition based feature extraction using band‐limited phase‐only correlation and discrete orthonormal Stockwell transform

Biometrics is the bureau of science that helps to measure the individual's features by utilising their behavioural and physiological characteristics. Since years ago biometric technology are contemplated to be a unique tool for other security purposes. Inauspicious biometrics, Ear recognition, and Finger Knuckle Print have been enchanted as a booming analysis with interests among several researchers in modern periods. It has an ample range of private and other law enforcement applications. A combination of multiple human attributes is authenticated and considered to be a competent strategy in case of a multimodal Personal authentication system. In this paper extracting the local and global features via the structure of the time-frequency domain has been studied. This proposed scheme exploits the analysis of dual biometric modalities i.e. Ear and Finger Knuckle Print which are carried out at the stage of feature-level fusion. The feature Extraction two biometric patterns are obtained by generating the Local and Global feature information that helps in refining the alignment of dual biometric images in matching i.e. Discrete Orth normal Stockwell Transform-Ear recognition and Band Limited phase-only correlation with Finger Knuckle Print. Experiment results conducted with these FKP and EAR are demonstrated in improving recognition of efficient accuracy.