Personal Identification System Research Articles

Design and Implementation of Multiple Biometrics Recognition Using Personal Identification System

A sole biometric identifier in construction an individual identification is often not able to meet the preferred performance requirements. Biometric identification pedestal on multiple biometrics correspond to an up-and-coming trend. computerized biometric scheme for person identification gauge a “signature” of the human body, compare the resulting characteristic to a database, and cause to be an request dependent decision. These biometric systems for personal authentication and identification are based upon physiological or behavioral features which are classically characteristic, even though point in time varying, such as Face recognition, Iris recognition, Fingerprint verification, Palm print verification in making a personal identification. Multi-biometric systems, which consolidate in sequence from multiple biometric sources, are in advance popularity since they are able to conquer limitations such as non-universality, noisy sensor data, large intra-user difference and vulnerability to spoof attacks that are commonly encountered in uni-biometric systems. In this paper, it addresses the concept issues and the applications strategies of multi-biometric systems.

Face and Liveliness Detection Based Smart Bank Locker

Face is one of the easiest ways to differentiate each other's identities. Facial recognition is a personal identification system that uses the personal characteristics of someone to identify who you are. The process of recognizing a person's face is basically it consists of two stages, namely facial recognition, in which this process occurs most frequently immediately to humans, except under circumstances where the object is available in a short period of time far, next is an introduction, which recognizes faces as individuals. Stage then it is repeated and developed as a model for face image recognition (face recognition) is one of the most widely studied biometric technologies and developed by experts. There are two types of currently popular methods of advanced facial recognition pattern namely, Eigen face method and Fisher face method. We use fisher face a monitoring method to verify any system. The location of this project is facing image processing system. The software requirements for this project are an anaconda.

Face Detection System with Face Recognition

Abstract: The face is one of the easiest way to distinguish the individual identity of each other. Face recognition is a personal identification system that uses personal characteristics of a person to identify the person's identity. Now a days Human Face Detection and Recognition become a major field of interest in current research because there is no deterministic algorithm to find faces in a given image. Human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is recognition, which recognize (by comparing face with picture or either with image captured through webcam) a face as an individual. In face detection and recognition technology, it is mainly introduced from the OpenCV method. Face recognition is one of the much-studied biometrics technology and developed by experts. The area of this project face detection system with face recognition is Image processing. The software requirement for this project is Python. Keywords: face detection, face recognition, cascade_classifier, LBPH.

Deep Learning Based Multi-Modal Biometric Security System Using Visible Light Communication

Multi-biometric system is an advanced technology which has a wide application space in the field of information security. This work proposes the design of a personal identification system based on a combination of biometric inputs such as face, finger vein, iris and fingerprint. Viola jones algorithm is used for face detection. Convolutional neural network (CNN) with different optimisers are used to steeply raise the image texture and extract high definition distinct features of the input images. The deep dream image algorithm accompanies CNN by visualizing these images and by highlighting the image features learnt by the network. These images are used for understanding and diagnosing network behaviour. This network obtains a high recognition rate, which proves to be better performing than traditional algorithms. In addition to these, a high-speed advanced wireless communication technology (Li-Fi) is used in combination with GSM which would act as an alert system that effectively helps in notifying the supervisory authority, if the system is being trespassed without proper authentication.

Person Identification With Millimeter-Wave Radar in Realistic Smart Home Scenarios

Compared with visual sensors that have light dependence and privacy intrusion issues, non-intrusion millimeter-wave (mmW) radars are more suitable for the daily person identification. In a realistic home scenario, there are new challenges that are not taken into account in the existing research. This letter attempts to address these issues such as multipath interference, complex walking process, and recognition robustness in smart home scenarios and designs a lightweight multi-branch convolutional neural network (CNN) with an Inception-Pool module and a Residual-Pool module to learn and classify gait Doppler features. The experimental results in a home living room scenario indicate that the designed mmW radar person identification system can achieve accurate and robust real-time identification performance.

Performance Evaluation of New Feature based on Ordinal Pattern Analysis for Iris Biometric Recognition

The Iris recognition technique is currently the most efficient biometric identification system and is a common system on the practical front. Though most of the commercial systems use the patented Daugman’s algorithm, which mainly uses wavelet-based features, research is still active in identifying novel features that can provide personal identification. Here the first novel proposal of using ordinal pattern measure based on nonlinear time series analysis is put forth to characterize the unique pattern of the iris of individuals and thereby perform personal identification. Dispersion Entropy is a nonlinear time-series analysis method highly efficient in the characterization of the complexity of any data series with proven effectiveness in the characterization of model system dynamics as well as real-world data series. The results show that dispersion entropy can be used to identify iris images of specific individuals. The efficiency of this method is evaluated by computing correlation and RMSE between dispersion entropy values of normalized iris image rubber sheet data. The experimental results on the popular IRIS database- CASIA v1- demonstrate that the proposed method can effectively perform differential identification of iris images from different individuals. The results specifically indicate that the density of information along the angular direction of iris images which falls along the rows of rubber sheet data. This can be efficiently utilized with the method or ordinal pattern characterization and proves to be having promising potential for being incorporated into biometrics personal identification systems.

Study of Ear Biometrics Based Identification System Using Machine Learning

Abstract: Within the biometric industry, computerized person identification using ear pictures is a hot topic. The ear, like other biometrics like the face, iris, and fingerprints, contains a huge number of particular and unique traits that may be used to identify a person. Due to the mask-wearing scenario, most face detection methods fail in this present international COVID-19 pandemic. The eardrum is a great data source for inactive person authentication since it doesn't necessitate the person we're attempting to pinpoint to cooperate, and the structure of the ear doesn't change significantly over time.. The acquisition of a human ear is also simple because the ear is apparent even while wearing a mask. An ear biometric system can enhance other biometric technology in an automated person identification system by giving authentication cues when other information is unreliable or even missing. We provide a six-layer deep convolutional architecture for ear identification in this paper. On the IITD ear dataset, the deep network's potential efficiency is assessed. The IITD has a detection performance of 97.36 percent for the deep network model and 96.99 percent for the IITD. When paired with a competent surveillance system, this approach can be beneficial in identifying people in a large crowd. Keywords: Biometrics, Person identification, IIT-D, Deep learning, Ear dataset

‘New normality’ of the covid-19 era: Opportunities, limitations, risks

The article considers some key aspects of the current transformation of social reality. The author argues that the announcement of the pandemic determined an unprecedented situation: humanity faces a completely different concept of reality. In particular, the breakthrough in the development of NBIC technologies (nano, bio, new information and cognitive technologies) contributes to the strengthening of the paradigm that absolutizes the technocratic component of civilizational development. Under the general depression and decline in economic activity, there is an explosive growth in the field of bioengineering, information and cognitive research. The new normality of the coronavirus era, associated with the unprecedented development of artificial intelligence systems, video surveillance technologies, geolocation and big data, in an unusually short time has created new existential and legal problems. The proclaimed threat to public health, being a significant goal-setting, has become a dominant justification for the introduction of serious innovations that allow the ruling elites to block civil rights, in particular, to legalize the use of tracking systems. Under the fight against the pandemic, the personal identification systems based on advanced technologies are being transformed from a security tool of law enforcement agencies into a tool of mass social engineering. Russia has come close to a new stage of digital transformation - a state-wide credential management system (the Unified Federal Information Register). Considering the experience of China in digital segregation, concerns arise: will digitalization turn into something dangerous as the algorithms of artificial intelligence improve, the use of biometric surveillance broaden, etc.? The article emphasizes that scanning the appearance and collecting information about citizens allows to create a gigantic array of data, the use of which can have unpredictable consequences, and the problem of their unauthorized use is not the main one. The power of algorithms, which allows to manipulate a person by means of continuously collected information about him, can turn into a new, sophisticated form of genocide.

Centre-of-Mass Based Gait Recognition for Person Identification

Background: Gait recognition focuses on the identification of persons from their walking activity. This type of system plays an important role in visual surveillance applications. The walking pattern of every person is unique and difficult to replicate by others. Objective: The present article focuses on to develop a person identification system based on gait recognition. Methods: In this article, a novel gait recognition approach is proposed to show how human body Centre-of-mass-based walking characteristics can be used to recognize unauthorized and suspicious persons when they enter in a surveillance area. Walking pattern varies from person to person mainly due to the differences in the footsteps and body movement. Initially, the background is modelled from the input video captured through static cameras deployed for security purpose. Foreground moving object in the individual frames is then segmented using the background subtraction algorithm. Centre-of-mass based discriminative features of various walking patterns are then studied using Support Vector Machine(SVM) classifier to identify each unique walking pattern. Results: The proposed system has been evaluated using a self-generated dataset containing a side view of various walking video clips. The experimental results demonstrate that the proposed system achieves an encouraging person identification rate. Conclusion: This work can be further extended to provide a general approach in developing an automatic person identification system in an unconstrained environment.

CONCEPTUAL ASPECTS OF THE USE OF BIBLIOMETRIC TECHNOLOGIES IN THE FIELD OF COUNTERACTION TO CRIMINAL OFFENSES IN THE AIRLINE INDUSTRY

The article specifies that systematic and complex scientific researches on problems of the use of bibliometric technologies in preventive activity regarding detection and neutralization of threats to entities-facilities of air transport, companies staff, institutions and organizations which work in this field have not been considered yet. The results of researches and scientific papers of representatives of the Ukrainian forensic community had been analyzed, which determined the need for theoretical-legal and forensic research in the field of identification and verification of a person with the use of biometric technologies to solve tasks of counteraction to crime and civilian tasks of managerial and controlling content. The article purpose is to define current problems of the development of the newest directions of biometric technologies use in counteraction to criminal and other types of offenses; formation of theoretical principles of algorithmization of the use of biometric technologies in preventive activity.
 The use of the complex of general and special research methods has made it possible to identify factors and content of reasons and conditions for solving the present day tasks with the use of biometric technologies in security and preventive activities. Such approach has helped to draw the conclusion that the most promising directions of biometric technologies application are: security guaranteeing and ensuring access control to certain areas and space activity facilities; creation of a person identification systems; introduction of reliable and economic means of delimitation of access to the territory of facilities, buildings and internal premises of space activity entities; efficiency and personnel management; information protection of multifunctional user identification systems in information networks, operating systems, various add-ons for typical software.

Personal identification systems for persons with dementia: a systematic review.

People with dementia can become missing and unable to be identified due to their cognitive decline and may not have belongings, including personally identifiable information. The aim of this systematic review was to summarise and discuss the current status of personal identification systems for missing and unidentified persons with dementia. An electronic systematic literature search was conducted by using PubMed, Web of Science, Cochrane Library, and CINAHL. By performing an electronic literature search, 128 texts were extracted. However, only three eligible texts were included in this review, and these were divided on the basis of the analysis target as follows: medical and human community support systems and digital systems. It is important to develop medical and social care systems and to construct and advance social personal identification systems, including medical and human community support systems and digital systems, for persons with dementia. However, this systematic review clarified that there have only been a few reports about the development and construction of social systems for missing and unidentified persons with dementia. People who interact with persons with dementia should consider this problem, which is increasing each year, and strive to build a social system that can be applied to personal identification as soon as possible.

Face Detection in Blurred Surveillance Videos for Crime Investigation

Face detection itself acts as a significant problem in low-resolution surveillance video, primarily due to out-of-focus blur. Real-time and forensic analysis for Law enforcement desires superior face recognition system for person identification in surveillance applications. However, face detection influences face recognition in critical situations. If faces on the blacklist are not detected, recognition fails, which increases the burden and is a tremendous challenge for police authority. This motivates us to present the deblurring algorithm to improve the face detection rate and reduce the false-positive rate. Hence, this paper focuses on removing blur by applying a blind deconvolution algorithm, suppressing the ringing artifact in surveillance video by adopting Discrete Wavelet Transform (DWT). Finally, after this preliminary work, faces are detected by Yolo v2. The proposed framework works well in the sparsely crowded scenario and improves face detection rate compared to the Lucy-Richardson-Yolo v2 framework, which suffers from the problem mentioned earlier. Experiments and evaluations on the surveillance dataset show that the proposed blind deconvolution with ringing suppression-based solution outperforms the state-of-the-art methods in detecting both frontal and profile faces.

Personal identification system based on multi biometric depending on cuckoo search algorithm

In modern devices, many personal identification systems are used using various biometrics to confirm the identity of an individual and identify him for several purposes. Some of these essential biometrics are used in this paper to help identify a person while attaining social distance because of the widespread epidemics. The features of the face, eyes, nose, and finally, the features of the mouth are used in this article. The work begins by detecting the parts of multibiometric from the input images using Viola-Jones face detection algorithm with a modification to it then segment them. After that, various initial treatment processes begin which helps clarify them to facilitate subsequent operations. Also, a Histogram of oriented gradient method(HOG) is used to extract the significant features of those image segments. The extracted features from these segments are entered into the developed cuckoo search algorithm(DCSA), the best image segment within the used dataset is searched for similar in terms of characteristics to the entered image segment. The work has also been developed so that the system is executed on two cores using parallel processing technology to utilize the processor as much as possible and reduce the time it takes to implement the system and identify the person concerned. A high identification rate has been reached, reaching 99.25%, and overall speed up 1.40323 sec relative to serial execution.

A Study on the Utilization of MobileElectronic Notice Service using Korean Digital Identity Guidelines

Recently, due to the spread of infectious diseases, the demand for services in a non-connected environment is increasing worldwide. Identifying the service counterpart in such a non-face-to-face environment has become the most important task.In this paper, we examine the status of overseas personal identification systems and explain the Korean digital identity guideline. Using the Korean digital identity guideline, it presents the application plan of mobile electronic notification service that has delivery and proof effect. For personal digital identity, Korea identifies citizens based on a registration number (RN) that is uniquely assigned at birth. However, if the RN is leaked or exposed, the problem of personal privacy invasion has emerged, and in the end, due to personal information protection issues, RNs cannot be collected unless required by law. Therefore, connecting information (CI) was utilized as a digital identity capable alternative to RN to clearly identify users online.CI has the advantage of being encrypted as a 1:1 match to the RN and not being able to identify an individual even if leaked.The mobile electronic notification service is designed to accurately deliver information to the target person in a non-face-to-face environment using CI.This service uses CI to identify the recipient of the electronic document, and after receiving identification from the personal identity proofing agency (PIPA), the service can be verified by reading the information.In this paper, we propose a method to easily provide information in the non-face-to-face era by presenting a method to apply to mobile electronic notification service (MENS) using Korean user identification guidelines.Through the proposed method, it can be confirmed that a system can be established to facilitate the mobile electronic notification service, which has the same effect as the delivery of information online and offline in the non-face-to-face era.

RETRACTED ARTICLE: Research on athlete recognition based on image feature extraction and artificial intelligence classification

With the continuous development of modern technology, the continuous update and evolution of information collection, storage and information processing technology and equipment, and the problems of information security, the development of electronic identity verification related technologies has continuously attracted widespread social attention. Athlete’s personal identification authentication refers to the effective information identification and automatic authentication of mobilized personal identity. Among them, the athlete’s personal identification function is used to help judge who he is. In the context of the current unstable international military, political, economic and social situation in the country and the still severe domestic anti-terrorism situation, in order to effectively protect the personal property, life and personal property of a country and its people, improve the grassroots level of our people the basic living standards of the masses, the development of convenient, efficient, and humanized national athlete personal identification information identification system technology has developed into urgent problems. In order to accurately simulate human machine images and accurately recognize facial features of human images, artificial intelligence has been widely used in the field of image recognition technology in recent years. Related scientific research staffs have proposed many recognition methods and calculation models. Through the training of the model training cluster, the corresponding input recognition target model is obtained. After the accuracy of the input target recognition image and the input model target are compared, the model input can be judged in the future whether the accuracy of the target image is the same as the model target input image. In summary, the main key point of image recognition is still to extract image features.

Towards online applications of EEG biometrics using visual evoked potentials

Electroencephalogram (EEG)-based biometrics have attracted increasing attention in recent years. A few studies have used visual evoked potentials (VEPs) in EEG biometrics due to their high signal-to-noise ratio (SNR) and good stability. However, a systematic comparison of different types of VEPs is still lacking. Therefore, this study proposes a system framework for VEP-based biometrics. We quantitatively compared the performance of three types of VEP signals in person identification. Flash VEPs (f-VEPs), steady-state VEPs (ss-VEPs), and code-modulated VEPs (c-VEPs) measured from a group of 21 subjects on two different days were used to estimate the correct recognition rate (CRR). We adopted a template-matching-based identification algorithm that was developed for VEP detection in brain-computer interfaces (BCIs) for person identification. Furthermore, this study demonstrates an online person identification system using c-VEPs with a group of 15 subjects. Among the three methods, c-VEPs achieved the highest CRRs of 100% using 3.15-s VEP data (a 5.25-s duration including 2.1-s intervals) in the intra-session condition and 99.48% using 10.5-s VEP data (a 17.5-s duration including 7-s intervals) in the cross-session condition. The online system achieved a cross-session CRR of 98.93% using 10.5-s VEP data (a 14-s duration including 3.5-s intervals). A systematic comparison of the performance of the three types of VEP signals in EEG-based person identification revealed that the c-VEP paradigm achieved the highest CRRs. The online system further demonstrated high performance in practical applications. The proposed VEP-based biometric system obtained promising identification performance, showing great potential for online person identification applications in real life.

Person Identification Using Structural Vibrations via Footfalls for Smart Home Applications

In this article, we present a person identification system for Internet-of-Things-based smart home applications that utilizes footstep induced structural vibrations as biometric modality. Footfall events are generated by the rhythmic contact of the heel and toe on the floor while walking. In the case of such biometric systems, there is no disturbance of the natural movement of the individuals and, thus, they provide an advantage over the existing systems that deal with human intervention. Another key advantage is that footfall signals are not subjected to spoofing attacks as they are nearly impossible to mimic unlike other biometric traits (fingerprint, face, and voice). We propose a 3-layer computing architecture, for the decentralized implementation of the biometric system. We also propose a basis pursuit-based data compression technique (DS8BP) to reduce power and bandwidth for the wireless transmission of footfall events. DS8BP achieves a compression ratio of 108 and increases the scalability of the system. We performed extensive experimentation to evaluate the proposed biometric system using indigenous databases containing 100,000+ footfall events of eight individuals collected in three types of surfaces (concrete tile floor, carpet floor, and wooden floor). The proposed system achieves a prediction accuracy of 93%, 98%, and 96% in three surface types when features from seven footsteps are considered.

Detection of spoofing attacks for ear biometrics through image quality assessment and deep learning

Ear recognition systems are one of the popular person identification systems. These biometric systems need to be protected against attackers. In this paper, a novel method is proposed to detect spoof attacks within ear recognition systems. The proposed method employs Convolutional Neural Network (CNN) which is based on deep learning and Image Quality Measure (IQM) techniques to detect printed photo attacks against ear recognition systems. Full-reference and no-reference image quality measures are used to extract ear image features. Score-level fusion is used to combine the scores obtained from image quality measures. Finally, decision-level fusion is employed to fuse the decisions obtained from CNN and IQM systems. The final decision is obtained as real or fake image as the output of the whole system. The experiments are conducted on publicly available ear datasets namely, AMI, UBEAR, IITD, USTB set 1 and USTB set 2 and the obtained results are compared with the state-of-the-art methods that are focused on printed photo attacks as well.

Person Identification From Audio Aesthetic

Behavioral biometrics survey actions rather than the physical traits of the person. Within this categorization, social behavioral biometrics utilizes an individual's communications for biometric analysis. The investigation of the uniqueness of human preferences and their implications to other aspects of an individual, such as personality or gender, is both a psychological and a biometric problem. An emerging approach is the usage of an individual's aesthetic preferences for the purpose of person identification. Recent research into the identification from visual aesthetics has found that these preferences hold significant discriminatory value. However, aesthetic identification has only been conducted through a visual medium via a set of liked images. The contribution of this work is the development of the first audio aesthetic preference system for person identification. The proposed system extracts descriptive intra-song and inter-song features from a set of songs favored by users and utilizes an ensemble of classifiers for prediction. The final decision is optimized by a genetic algorithm. Experimental results demonstrate that the developed audio aesthetic system achieves 95% user recognition accuracy on both proprietary and public audio datasets.

PCANet based biometric system with fusion of palmprint and dorsal hand vein

Nowadays, Biometric systems are prevalent for personal recognition. But due to pandemic COVID 19, it is difficult to pursue a touch-based biometric system. To encourage a touchless biometric system, a less constrained multimodal personal identification system using palmprint and dorsal hand vein is presented. Hand based Touchless recognition system gives a higher user-friendly system and avoids the spread of coronavirus. A method using Convolution Neural Networks(CNN) to extract discriminative features from the data samples is proposed. A pre-trained function PCANeT is used in the experiments to show the performance of the system in fusion scheme. This method doesn’t require keeping the palm in a specific position or at a certain distance like most other papers. Different patches of ROI are used at two different layers of CNN. Fusion of palmprint and dorsal hand vein is done for final result matching. Both Feature level and score level fusion methods are compared. Results shows the accuracy of upto 98.55% and 98.86% and Equal error rate (EER) of upto 1.22% and 0.93% for score level fusion and feature level fusion, respectively. Our method gives higher accurate results in a less constrained environment.