Personal Identification Method Research Articles

An Empirical Investigation of Human Identity Verification Methods

A recognition technique is essential in practically every industry in the current digital era. It has several advantages and may be used for security, identification, and authentication. The relevance of access control systems based on biometrics has grown in recent years since they have the ability to address the majority of the shortcomings of existing security systems. Automated biometric systems for human identification take a measurement of the body's "signature," compare it to a database, and make an application-specific determination. These biometric methods for personal verification and identification are based on physiological or behavioral traits that are usually recognizable, despite changing over time, such as fingerprints, hand geometry, the face, voice, lip movement, gait, and iris patterns. The purpose of this study is to conduct a thorough literature review in order to pinpoint the most well-known recognition techniques, applications, and obstacles.

Research on Personal Credit Risk Assessment Based on Data Fusion

In recent years, the domestic emphasis on systemic risk prevention has continued to increase. Chinese credit department, which is a crucial department to avoid significant systemic risks, has a particular responsibility to manage and control personal credit risks related to the lives of the public. Therefore, strengthening the management of the individual credit department, especially to guard against personal credit risks, build a data-driven quantitative model for scoring applicants’ credit, and improve the personal credit identification and evaluation methods on this basis is a vital part of individual credit risk prevention and systemic financial risk. In this paper, we first study the index system of personal credit risk identification, standard personal credit risk identification models, and some data fusion techniques. Then, we build an individual credit risk identification model based on the data fusion method on the above theoretical basis. After that, the selection of indicators was carried out among 20 variables of German credit data. Finally, an empirical study was conducted to compare the effect of the two data fusion methods on the performance of the individual credit risk identification model. The defects of single models in the personal credit risk problem domain are apparent. However, data fusion technology can help classification models to gain better uniformity in accuracy and robustness, but it also needs to be improved in terms of interpretability and promotion.

Denture marking for personal identification in forensic odontology: A narrative review

Aim: In certain situations, other forensic aids fail to provide proper personal identification such as changes after death, tissue injury trauma, burns, and insufficiency of fingerprint evidence. Dental identification plays a crucial role in this aspect. However, in edentulous patients, who do not possess teeth and the alveolar bone resorbs very fast, denture marking is a reliable aid for easy identification. This review was carried out to investigate how many methods of denture marking are listed in the literature for personal identification to date and which method is the most reliable?Materials and Methods: Following PRISMA principles, a search of the published literature was conducted electronically in PubMed/Medline, ResearchGate, Google Scholar, and Scopus databases, without the publication year limitation using keywords such as (“Denture marking,” OR “Denture labelling”) AND (“Prosthesis”), AND (“Personal identification”), AND (“Forensic science” OR “Forensic odontology”). A manual search of all related journals and reference lists of the relevant articles was also performed. Results: The database search yielded a total of 140 articles; out of which 45 relevant articles were selected describing 10 surface denture marking methods and 20 inclusion denture marking methods. Each method is enclosed with a few of its advantages and disadvantages. Conclusion: From this research, it can be concluded that at present, 30 denture marking methods for personal identification have been reported in the literature, but it cannot be stated definitely which method is the most reliable, as the strengths and weaknesses of each method supersede each other. Robust research and a number of population-based studies are required in this context.

Semi-automatic fingerprint image restoration algorithm using a partial differential equation

<abstract><p>A fingerprint is the unique, complex pattern of ridges and valleys on the surface of an individual's fingertip. Fingerprinting is one of the most popular and widely used biometric authentication methods for personal identification because of its reliability, acceptability, high level of security, and low cost. When using fingerprints as a biometric, restoring poor-quality or damaged fingerprints is an essential process for accurate verification. In this study, we present a semi-automatic fingerprint image restoration method using a partial differential equation to repair damaged fingerprint images. The proposed algorithm is based on the Cahn-Hilliard (CH) equation with a source term, which was developed for simulating pattern formation during the phase separation of diblock copolymers in chemical engineering applications. In previous work, in order to find an optimal model and numerical parameter values in the governing equation, we had to make several trial and error preliminary attempts. To overcome these problems, the proposed novel algorithm minimizes user input and automatically computes the necessary model and numerical parameter values of the governing equation. Computational simulations on various damaged fingerprint samples are presented to demonstrate the superior performance of the proposed method.</p></abstract>

Wybrane aspekty wdrażania i wykorzystania w praktyce nowych rozwiązań kryminalistycznych

Technological progress creates new human living environment and exerts significant influence on its actions, including criminal activities. It is the responsibility of the law enforcement authorities to constantly improve the technical and tactical methods of crime detection. As a consequence, attention is directed towards forensic sciences. Improving technical and tactical solutions is part of ist tasks. Forensic sciences are not passive in the face of expectations regarding the improvement of personal identification methods, as well as other solutions (investigative tools, equipment of forensics). The conducted research projects clearly prove that. Other issue is the process of implementing new solutions, which is often time-consuming and costly. The often skeptical way of perceiving way new achievments by representatives of criminal process does not make the task easier. Generally, successful implementation is determined by many factors. Technical features of the new solutions are certainly important but legal and ethical requirements are also relevant.

Determining The Sex of a Person by Odontometric Parameters Using Logistic Regression Equations

The development of personal identification methods based on bone remains, which are used to diagnose the population of Azerbaijan, is a priority task due to the unresolved issue. In this scenario, a new method for quickly determining sex by teeth is in demand, as it should increase the efficiency of the work of forensic experts. The purpose of this study was to develop modern methods for diagnosing human sex using odontometric studies. At the craniological collection of Azerbaijanis, the sizes of teeth were studied. On 7 teeth of the upper and 7 teeth of the lower jaw, 3 parameters were measured (in this case, symmetrical teeth were considered as a single array). Each of the 14 anatomical teeth was represented by 80 male and 80 female teeth. For odontometric procedures, a Mutitouyo digital caliper (accuracy 10 microns) was used. The results of odontometry were analyzed by statistical methods using ROC analysis. Mathematical modeling was carried out using the MATLAB statistical software package. With the help of ROC-analysis, the data obtained during odontometry were considered. 6 odontometric signs were established, which are interconnected with gender. The most correlated with gender were the bucco-lingual diameter of the upper canine, as well as the heights of the crowns – the second lower molar, lower incisors, lower canine and upper medial incisor. Using mathematical modeling, we have developed 6 logistic regression equations that can be used to determine the gender of a person by the size of his teeth. The predictive potential of the equations was also evaluated using ROC analysis procedures. As a result of the study, based on odontometric indicators and by using ROC analysis, diagnostic equations were obtained to determine the sex of a person by the size of the teeth. The equations have not yet been verified on independent samples, so they cannot yet be unconditionally recommended for practice. Nevertheless, the authors count on the high legitimacy of their own models due to the fact that they used a fairly powerful mathematical apparatus.

Contactless Palmprint Recognition Using Binarized Statistical Image Features-Based Multiresolution Analysis.

In recent years, palmprint recognition has gained increased interest and has been a focus of significant research as a trustworthy personal identification method. The performance of any palmprint recognition system mainly depends on the effectiveness of the utilized feature extraction approach. In this paper, we propose a three-step approach to address the challenging problem of contactless palmprint recognition: (1) a pre-processing, based on median filtering and contrast limited adaptive histogram equalization (CLAHE), is used to remove potential noise and equalize the images' lighting; (2) a multiresolution analysis is applied to extract binarized statistical image features (BSIF) at several discrete wavelet transform (DWT) resolutions; (3) a classification stage is performed to categorize the extracted features into the corresponding class using a K-nearest neighbors (K-NN)-based classifier. The feature extraction strategy is the main contribution of this work; we used the multiresolution analysis to extract the pertinent information from several image resolutions as an alternative to the classical method based on multi-patch decomposition. The proposed approach was thoroughly assessed using two contactless palmprint databases: the Indian Institute of Technology-Delhi (IITD) and the Chinese Academy of Sciences Institute of Automatisation (CASIA). The results are impressive compared to the current state-of-the-art methods: the Rank-1 recognition rates are 98.77% and 98.10% for the IITD and CASIA databases, respectively.

Stature estimation by semi-automatic measurements of 3D CT images of the femur

Stature estimation is one of the most basic and important methods of personal identification. The long bones of the limbs provide the most accurate stature estimation, with the femur being one of the most useful. In all the previously reported methods of stature estimation using computed tomography (CT) images of the femur, laborious manual measurement was necessary. A semi-automatic bone measuring method can simplify this process, so we firstly reported a stature estimation process using semi-automatic bone measurement software equipped with artificial intelligence. Multiple measurements of femurs of adult Japanese cadavers were performed using automatic three-dimensional reconstructed CT images of femurs. After manually setting four points on the femur, an automatic measurement was acquired. The relationships between stature and five femoral measurements, with acceptable intraobserver and interobserver errors, were analyzed with single regression analysis using the standard error of the estimate (SEE) and the coefficient of determination (R2). The maximum length of the femur (MLF) provided the lowest SEE and the highest R2; the SEE and R2 in all cadavers, males and females, respectively, were 3.913 cm (R2 = 0.842), 3.664 cm (R2 = 0.705), and 3.456 cm (R2 = 0.686) for MLF on the right femur, and 3.837 cm (R2 = 0.848), 3.667 cm (R2 = 0.705), and 3.384 cm (R2 = 0.699) for MLF on the left femur. These results were non-inferior to those of previous reports regarding stature estimation using the MLF. Stature estimation with this simple and time-saving method would be useful in forensic medical practice.

A Model for Unauthorized Person access in a Particular Area

Abstract: A method of unauthorized person identification in a particular area of an entity includes maintaining a database of identification data specific to the person and condition of entries and time period, providing a unique description for each person enabling access to the person identification data in the database. The automated face recognition system uses a new face-finding and an automated eye- finding approach combined with an intelligent metric. This allows templates to be placed on any size chip or into two-dimensional (2-D) barcodes for selfauthenticating documents, as well as for quick easy transmission over the internet, wireless devices, or Ethernet (i.e., LAN, WAN, etc.). A biometric and identity enrollment camera for collecting personal data includes a slidable main module and at least one modifiable section removable coupled to the main module. The main module includes a processor and one or more biometric sensing devices coupled to the processor. Methods of detecting authorized person identity, differentiating between users of a computerized service, and detecting an unauthorized person. The data management facility may be geographically distributed at a plurality of data management sites and the data storage nodes may exist inside and outside of a particular area of the first person.

CAMERA BASED FINGERPRINT AUTHENTICATION SYSTEM USING MATLAB

— “At the end of the day, the goals are simple: safety and security” -Jodi Rell The above quote shows the importance of security in the whole society. We can see how safety and security is playing an important role in our day-to-day life. Despite the multitude of personal identification methods currently in practice, fingerprint verification has traditionally been considered one of the most reliable methods available. But is it the safest means during the time of COVID-19 pandemic? Our traditional scanner-based fingerprint authentication system requires the major upgrade to sustain in the current scenario. Can whole process be contactless…….

A New Wave in Biometric System: Systematic Study

Biometric system is a technique used to identify a person using its personal identification methods. The main concept of biometric systems is to provide confidentiality and security to the user. A number of biometric systems are introduced but some systems are widely used and are famous because of their usage and security they provide. Physiological and Behavioral biometrics are the two types of biometric systems. Biometric systems include physiological biometrics like face recognition, fingerprint recognition, iris recognition and behavioral biometrics like signature recognition and voice recognition. All these recognition systems are discussed in this research paper. Biometric systems work on three levels: Enrollment, Verification, and Identification. Enrollment is the process in which patterns are captured from the user and stored in the database. Verification means to confirm that the sample entered by the user belongs to him or not. When the user wants to access the data then the user must use his/her biometrics so that the system checks that the person who wants to access the data is the real owner of the data or not. This process is identification. All three levels are the working levels of the Biometric System. In earlier years, biometrics were used only at ground levels to provide basic security to data but now the tables have turned. It is playing a major role in providing security to our data. Biometrics are not only used in day-to-day life in phone unlocking, phone assistants, attendance systems but also used at advanced levels like in airports, border security, cloud computing etc. In this research paper, we will discuss the future scope of biometric systems and how it could even change the future.

The effectiveness of logistic regression equations for solving problems of determining the sex of a person by odontometric parameters

New methods of personal identification based on bone remains are always of interest to researchers. The new method proposed by us for rapid determination of sex by teeth is also likely to be in demand, since it should increase the efficiency of the work of forensic experts.The purpose of this study was to develop modern methods for diagnosing human sex using odontometric studies.At the craniological collection of Azerbaijanis, the sizes of teeth were studied. On 7 teeth of the upper and 7 teeth of the lower jaw, 3 parameters were measured (in this case, symmetrical teeth were considered as a single array). Each of the 14 anatomical teeth was represented by 1120 male and 1120 female teeth. For odontometric procedures, a Mutitouyo digital caliper (accuracy 10 microns) was used. The results of odontometry were analyzed by statistical methods using ROC analysis. Mathematical modeling was carried out using the MATLAB statistical software package.With the help of ROC-analysis, the data obtained during odontometry were considered. 6 odontometric signs were established, which are interconnected with gender. The most correlated with gender were the bucco-lingual diameter of the upper canine, as well as the heights of the crowns – the second lower molar, lower incisors, lower canine and upper medial incisor. Using mathematical modeling, we have developed 6 logistic regression equations that can be used to determine the gender of a person by the size of his teeth. The predictive potential of the equations was also evaluated using ROC analysis procedures.As a result of the study, based on odontometric indicators and by using ROC analysis, diagnostic equations were obtained to determine the sex of a person by the size of the teeth. The authors have not yet tested the effectiveness of the equations on independent samples of skulls, so they cannot yet be unconditionally recommended for practice. However, they count on the high legitimacy of their own models, because they used a fairly powerful mathematical apparatus.

Roles of Dental Care in Disaster Medicine in Japan.

Purpose of ReviewNatural disasters occur frequently in Japan. A disaster medical system was rapidly developed in Japan following the Great Hanshin-Awaji Earthquake in 1995. Dentistry has become increasingly important in disaster medicine. This review summarizes the roles of dental professionals in disaster medicine, highlights relevant issues, and identifies new directions for research to improve disaster relief activities based on our previous experiences as dental professionals supporting the victims of major disasters.Recent FindingsMany preventable deaths after a disaster are caused by aspiration pneumonia, which occurs against a background of factors that are compounded by a harsh living environment. An important aim of dental care in disaster medicine is to prevent these disaster-related deaths in vulnerable persons such as the elderly. This can be achieved through interventions to maintain oral hygiene, preserve and enhance oral function (i.e., chewing and swallowing), and improve the diet, since these interventions help to prevent the development of malnutrition and frailty in vulnerable people. Dental identification of disaster victims could be improved through the use of intraoral three-dimensional scanners and artificial intelligence to automate the acquisition of dental findings and through the construction of a national database of digitized dental records. Advances in personal identification methods will be needed given the prediction that a catastrophic earthquake will occur on the Nankai Trough during the next 30 years and claim more victims than the 2011 Great East Japan Earthquake.SummaryDisaster-related deaths due to aspiration pneumonia can be prevented by providing appropriate dental care to those in need. The process of identifying victims could be made more efficient through the use of intraoral three-dimensional scanning, artificial intelligence, and a digital database of dental records. Establishing and strengthening relationships between professionals in different regions will help to optimize the multidisciplinary response to future large-scale disasters.

Person identification from arm’s hair patterns using CT-twofold Siamese network in forensic psychiatric hospitals

Identifying criminals in serious crimes from digital images is a challenging forensic task as their faces will be covered in most cases. In addition, the only available information will be hand. A single robust technique to identify the criminals from arm’s hair patterns can be a potential cost-effective and unobtrusive solution in various other areas such as in criminal psychiatric hospitals during rehabilitation to identify and track patients instead of using barcoding, radio frequency identification (RFID), and biometrics. The existing state-of-the-art methods for person identification uses convolutional neural network (CNN) and long short-term memory (LSTM)-based architectures which require the entire data to be trained once again when new data comes. To address these issues, we proposed a novel Siamese network-based architecture which not only reduces this training paradigm but also performs better than several existing methods. Since there were no standard datasets for person identification from arm’s hair patterns, we created a database with several voluntary participants by collecting their hands’ images. Several data augmentation techniques are also used to make the database more robust. The experimental results show that the proposed architecture performs better for the created database with mAP, mINP, and R1 of 94.8, 90.0, and 93.5, respectively. The proposed CTTSN performs well for the closed world person re-identification problem using soft biometric features in real time (52 frames per second).

Multi-Session Surface Electromyogram Signal Database for Personal Identification

Surface electromyogram (sEMG) refers to a biosignal acquired from the skin surface during the contraction of skeletal muscles, and a different signal waveform is generated, depending on the motion performed. Therefore, in contrast to generic personal identification, which uses only a piece of registered information, the sEMG changes the registered information in a personal identification method. The sEMG database (DB) for conventional personal identification has shortcomings, such as a few subjects and the inability to verify sEMG signal variability. In order to solve the problems of DBs, this paper describes a method for constructing a multi-session sEMG DB for many subjects. Data were obtained in two channels when each of the 200 subjects performed 12 motions. There were three sessions, and each motion was repeated 10 times in time intervals of a day or longer between each session. Furthermore, to verify the effectiveness of the constructed sEMG DB, we conducted a personal identification experiment. According to the experimental results, the accuracy for five subjects was 74.19%, demonstrating the applicability of the constructed multi-session sEMG DB.

Multidimensional nearest neighbors classification based system for incomplete lip print identification

This paper presents a method of personal identification via the analysis of lip print images. This remains a little explored field even within the most serious and leading biometric research teams. Biometrics is the scientific study of the identification and verification of individuals based on their physiological and behavioral traits. Such traits are permanent, unique, and can be used to separately identify any one individual from any another. In the method we present here, we integrated complex image processing techniques, machine learning, and statistical methods. We then evaluated this new method on previously collected realistic (i.e. low quality) real-world lip print images. Multi-variant experimental protocols, specifically designed for this work, then confirmed the accuracy of our new technique. Our results have extended the knowledge of – and the collection of methods available for – the identification of biometric objects from incomplete data sets. Biometric analysis techniques are rapidly gaining in importance. The approach we propose here will be useful in many areas including biometrics, forensics, and forensic medicine. The novelty of this proposed method is its ability to work on lip print images that are partially corrupted or incomplete, as often occurs in practice. Low quality areas are recognized and effectively eliminated. These areas are neither taken into account during the classifier learning process nor later during the samples’ classification. This was confirmed in a series of experiments in which the best classification accuracy achieved was 94.40%.

Identifying persons from iris images using neural networks for image segmentation and feature extraction

The problem of personal identification plays an important role in information security. In recent years, biometric methods of personal identification have become most relevant and promising. The article presents a study of a method for identifying a person from iris images using a neural network approach at the stages of segmentation and a feature representation from the data. A description of a dataset used to implement the segmentation stage using convolutional neural networks is presented and access to the segmentation masks of the entire dataset is provided. A method is proposed for extracting a feature representation of the data using pretrained convolutional neural networks to solve a problem of iris classification. A comparative analysis of methods for extracting iris features, including classical approaches and a neural network approach, has been carried out. A comparative analysis of classification methods is carried out, including classical machine learning algorithms, namely, support vector machines, random forest, and a k-nearest neighbors method. The results of experimental studies have shown the high quality of the classification based on the proposed approach.

Possibilities and challenges in digital personal identification using teledentistry based on integration of telecommunication and dental information: a narrative review.

Personal identification using dental evidence is decisive in the case of unidentified bodies because dental features, including decayed, missing and filled teeth, are unique to every individual. This review explored the possibilities and problems associated with personal identification through digital methods by the integration of telecommunications and dentistry, also known as teledentistry. Establishment of a global, objective personal identification method using dental evidence through teledentistry is desirable. However, the review reports that there are various problems that need to be resolved first, in aspects such as changes in dentition over time, technology, individual privacy and ethics. It is suggested that solving such problems and constructing a worldwide dental database for personal identification from dental images, using teledentistry, as part of social services, is a new challenge for researchers in forensic odontology and dentistry.

심전도의 다양한 2차원 변환에 의한 합성곱 신경망기반 개인식별

In this paper, we propose personal identification method based on Convolutional Neural Networks (CNN) by various two-dimensional (2D) transform of Electrocardiogram (ECG) signals. For this purpose, various 2D time-frequency representation are peformed by Short-Time Fourier Transform (STFT), Fourier Synchrosqueezed Transform (FSST), and Wavelet Synchrosqueezed Transform (WSST) from one-dimensional ECG signals. The individual identification performance is achieved by transfer learning based on the pretrained GoogleNet and ResNet-101. The performance of experimental results are compared by the well-known PTB-ECG database.

Lip print evaluation of Indian and Malaysian-Chinese subjects by manual and digital methods: a correlational study with gender and ethnicity

BackgroundCheiloscopy is a reliable method of personal identification which may augment the established methods like dactylography, DNA (deoxyribonucleic acid) profiling, and dental records.AimThis study aimed to determine the correlation of lip prints with ethnicity and gender of individuals in an attempt to bridge the gap between conventional manual methods and digital methods of cheiloscopy.MethodsLip prints of 300 gender-matched subjects of Indian and Malaysian-Chinese descents were collected and analyzed using the Suzuki K and Tsuchihashi Y classification system. The lip sizes were measured and lip print patterns were analyzed. The analysis was carried out using manual and computer-aided methods. A customized software for lip print analysis and validate it with the manual lip print analysis was developed.ResultsIndependent sample t test showed a statistically significant difference between the width and length of the lips between males and females of the total population (p < 0.001). Pearson’s chi-square test showed no statistically significant difference between the Indian males and females in the width of the lower lip (p = 0.074). In the Malaysian-Chinese population, there was a statistically significant difference between males and females in the length of the upper lip (p = 0.032) and lower lip (p = 0.035). The type V grooves were predominant in the total study population (51.3%) followed by type III pattern (38.7%). The new customized software could not provide reliable results.ConclusionsLip sizes differed significantly among the Indian and Malaysian-Chinese subjects. There was no significant gender dimorphism in the distribution of lip print patterns. The results from manual and computer-aided methods were comparable.