Human Identification Method Research Articles

WiFi-Based Human Identification with Machine Learning: A Comprehensive Survey.

In the modern world of human-computer interaction, notable advancements in human identification have been achieved across fields like healthcare, academia, security, etc. Despite these advancements, challenges remain, particularly in scenarios with poor lighting, occlusion, or non-line-of-sight. To overcome these limitations, the utilization of radio frequency (RF) wireless signals, particularly wireless fidelity (WiFi), has been considered an innovative solution in recent research studies. By analyzing WiFi signal fluctuations caused by human presence, researchers have developed machine learning (ML) models that significantly improve identification accuracy. This paper conducts a comprehensive survey of recent advances and practical implementations of WiFi-based human identification. Furthermore, it covers the ML models used for human identification, system overviews, and detailed WiFi-based human identification methods. It also includes system evaluation, discussion, and future trends related to human identification. Finally, we conclude by examining the limitations of the research and discussing how researchers can shift their attention toward shaping the future trajectory of human identification through wireless signals.

Limit Detection of Short Tandem Repeats (STR) Analysis on Touch DNA Samples

Forensic short tandem repeats (STR) profiling on touch DNA samples has emerged as a primary method for human identification. The stability and uniqueness of STR combination from the targeted locus in each individual make it a precision marker for human identification. Touch DNA samples can be found in traces of biological material shed from a person. This work aimed to identify the lowest concentration limit required for generating an interpretable DNA profile and the sensitivity of the STR loci applied. Touch DNA samples were collected from donors who were asked to hold a rope for 5 min. A double swab technique was used to lift the touch samples from the rope. These samples are subjected to DNA extraction and quantification. Two STR amplification cycles, 29 and 34 cycles, were used. DNA concentration greatly influences the success of amplifying the target allele at each STR locus to be interpreted into a complete DNA profile, shown by its allele peak. Touch DNA concentration >0.25 ng can produce a complete DNA profile. LCN method successfully amplified touch DNA with a concentration 0.0625–0.25 ng/µL. Limit detection of touch DNA analysis is 0.25 ng/µL. Low-copy DNA can still be analyzed within 0.0625–0.25 ng/µL.

Mitigating the Threat of Invasive Mosquito Species Expansion: A Comprehensive Entomological Surveillance Study on Kastellorizo, a Remote Greek Island.

The expansion of the tiger mosquito, a vector that can transmit diseases such as dengue, chikungunya, and Zika virus, poses a growing threat to global health. This study focuses on the entomological surveillance of Kastellorizo, a remote Greek island affected by its expansion. This research employs a multifaceted approach, combining KAP survey (knowledge, attitude, practices), mosquito collection using adult traps and human landing catches, and morphological and molecular identification methods. Results from questionnaires reveal community awareness and preparedness gaps, emphasizing the need for targeted education. Mosquito collections confirm the presence of the Aedes albopictus, Aedes cretinus, and Culex pipiens mosquitoes, highlighting the importance of surveillance. This study underscores the significance of community engagement in entomological efforts and proposes a citizen science initiative for sustained monitoring. Overall, this research provides essential insights for developing effective mosquito control programs in remote island settings, thereby emphasizing the importance of adopting a One Health approach to mitigate the spread of vector-borne diseases.

Distance-based integration method for human skin type identification

Although identifying human skin types is essential in dermatology, cosmetology, and facial recognition, the classification of human skin types is challenging due to the complex nature, varied characteristics, and the influence of external factors. Traditional methods for skin type identification often rely on subjective assessments, leading to inconsistent and inaccurate results. Therefore, this paper proposes a novel method named a distance-based integration method to identify skin types based on the Fitzpatrick skin scale, also known as the Fitzpatrick skin type. This study focuses on the objective distance measurement, integrated with the Fuzzy Analytic Hierarchy Process (AHP). The objective distance was utilized to determine the distance between each HEX color code for a clinical image and each target skin type. The Fuzzy AHP algorithm was employed to calculate the total score for each target class to identify human skin type. For this study, 1,022 images of human skin were used in the experiment. The results indicated that the proposed method achieved a high average accuracy of 93 %, precision of 80 %, and specificity of 96 %.

The Effects of AI-Driven Face Restoration on Forensic Face Recognition

In biometric recognition, face recognition is a mature and widely used technique that provides a fast, accurate, and reliable method for human identification. This paper aims to study the effects of face image restoration for forensic face recognition and then further analyzes the advantages and limitations of the four state-of-the-art face image restoration methods in the field of face recognition for forensic human image identification. In total, 100 face image materials from an open-source face image dataset are used for experiments. The Gaussian blur processing is applied to simulate the effect of blurred face images in actual cases of forensic human image identification. Four state-of-the-art AI-driven face restoration methods are used to restore the blurred face images. We use three mainstream face recognition systems to evaluate the recognition performance changes of the blurred face images and the restored face images. We find that although face image restoration can effectively remove facial noise and blurring effects, the restored images do not significantly improve the recognition performance of the face recognition systems. Face image restoration may change the original features in face images and introduce new made-up image features, thereby affecting the accuracy of face recognition. In current conditions, the improvement in face image restoration on the recognition performance of face recognition systems is limited, but it still has a positive role in the application of forensic human image identification.

A method for human behavior identification based on integrated sensor data using XGBoost classifier with PCA techniques

In recent decades, there has been a noticeable increase in the recognition among professionals of the importance of human acts. The identification of human activity has gained significant prominence because of its wide-ranging applications in several domains, including healthcare and recreational activities. Moreover, a substantial body of research in the field of machine learning has been dedicated to the development of methodologies aimed at automating the identification and analysis of human behavior. This increased interest is mostly due to the fact that there are now more tools that can collect information about how people live their daily lives. The data utilized in this study is readily available for public access on the Internet. The data set under consideration comprises sensor readings from several components integrated inside the smartphone, including the global positioning system (GPS), accelerometer, magnetometer, and gyroscope. The data sets are indifferent to the categories, positions, or alignments of the items. The data set is of considerable magnitude due to its collection from several sensors, including GPS, accelerometer, magnetometer, and gyroscope. Consequently, we are employing the Principal Component Analysis (PCA) technique to diminish the dimensionality of the data and enhance its precision. Our recommendation is to utilize the XGBoost classifier in combination with Principal Component Analysis (PCA). The recommended model had a total identification rate of 97.58%. In order to demonstrate the flexibility of the proposed method, we employ a 10-fold cross-validation technique, together with the utilization of a confusion matrix and ROC curve. These evaluation metrics serve to provide a tangible illustration of the recommended strategy. The proposed methodology might have encouraging outcomes in effectively discerning human behaviors, thereby potentially facilitating the restoration of walking and pushing gaits into a Bipedal Robot and other Parkinson’s diseases.

Analysis of human errors in human-autonomy collaboration in autonomous ships operations through shore control experimental data

Human-autonomy collaboration plays a pivotal role in the development of Maritime autonomous surface ships (MASS), as Shore control center (SCC) operators may engage in the control loop by directly operating the MASS, or, in the supervisory loop, monitoring the MASS and taking over control when needed. Thus, effective human performance during takeover control and operation is crucial for the safety of MASS operations. However, since the MASS is still in the early phase of development, the mechanism of human errors is unknown, and the data on human-autonomy collaborative operation is scarce. Human reliability analysis (HRA) aims to assess human errors qualitatively and quantitatively, and is widely used in various complex systems to help safety analysis. This study is dedicated to incorporating advanced HRA methods elements to identify and quantify human errors during taking over control and operation of a MASS in collision avoidance scenarios. It presents virtual experimental results, combined with theoretical human error identification and assessment methods. At first, we apply the Human-System Interaction in Autonomy (H-SIA) method to identify potential human errors; secondly, we identify relevant Performance Shaping Factors (PSFs) including Experience, Boredom, Task complexity, Available time and Pre-warning, and performance measures of the human errors, and implement them in the virtual experiment based on a full-scale autonomous ferry research vessel called milliAmpere2. Finally, we build a Bayesian Network (BN) to present causal and probabilistic relationships between PSFs and human errors through experimental data. The results show that available time has the highest impact on takeover performance of operators, followed by task complexity and pre-warning. Boredom does not present a significant sole impact unless combined with available time. Experience does not show a significant impact on human performance. In addition to the relevance of the human errors analysis to the safe development and operational design of MASS, the developed method benefits other human-autonomy collaborative systems. The developed BN model shows adaptability to assess human error probabilities, and the practical significance of integrating experimental data into the existing HRA methodologies for complex systems.

A developmental validation of the Quick TargSeq 1.0 integrated system for automated DNA genotyping in forensic science for reference samples.

Analysis of short tandem repeats (STRs) is a global standard method for human identification. Insertion/Deletion polymorphisms (DIPs) can be used for biogeographical ancestry inference. Current DNA typing involves a trained forensic worker operating several specialized instruments in a controlled laboratory environment, which takes 6-8h. We developed the Quick TargSeq 1.0 integrated system (hereinafter abbreviated to Quick TargSeq) for automated generation of STR and DIP profiles from buccal swab samples and blood stains. The system fully integrates the processes of DNA extraction, polymerase chain reaction (PCR) amplification, and electrophoresis separation using microfluidic biochip technology. Internal validation studies were performed using RTyper 21 or DIP 38 chip cartridges with single-source reference samples according to the Scientific Working Group for DNA Analysis Methods guidelines. These results indicated that the Quick TargSeq system can process reference samples and generate STR or DIP profiles in approximately 2h, and the profiles were concordant with those determined using traditional STR or DIP analysis methods. Thus, reproducible and concordant DNA profiles were obtained from reference samples. Throughout the study, no lane-to-lane or run-to-run contamination was observed. The Quick TargSeq system produced full profiles from buccal swabs with at least eight swipes, dried blood spot cards with two 2-mm disks, or 10ng of purified DNA. Potential PCR inhibitors (i.e., coffee, smoking tobacco, and chewing tobacco) did not appear to affect the amplification reactions of the instrument. The overall success rate and concordance rate of 153 samples were 94.12% and 93.44%, respectively, which is comparable to other commercially available rapid DNA instruments. A blind test initiated by a DNA expert group showed that the system can correctly produce DNA profiles with 97.29% genotype concordance with standard bench-processing methods, and the profiles can be uploaded into the national DNA database. These results demonstrated that the Quick TargSeq system can rapidly generate reliable DNA profiles in an automated manner and has the potential for use in the field and forensic laboratories.

Population genetic analyses of Eastern Chinese Han nationality using ForenSeq™ DNA Signature Prep Kit.

Currently, the most commonly used method for human identification and kinship analysis in forensic genetics is the detection of length polymorphism in short tandem repeats (STRs) using polymerase chain reaction (PCR) and capillary electrophoresis (CE). However, numerous studies have shown that considerable sequence variations exist in the repeat and flanking regions of the STR loci, which cannot be identified by CE detection. Comparatively, massively parallel sequencing (MPS) technology can capture these sequence differences, thereby enhancing the identification capability of certain STRs. In this study, we used the ForenSeq™ DNA Signature Prep Kit to sequence 58 STRs and 94 individual identification SNPs (iiSNPs) in a sample of 220 unrelated individuals from the Eastern Chinese Han population. Our aim is to obtain MPS-based STR and SNP data, providing further evidence for the study of population genetics and forensic applications. The results showed that the MPS method, utilizing sequence information, identified a total of 486 alleles on autosomal STRs (A-STRs), 97 alleles on X-chromosome STRs (X-STRs), and 218 alleles on Y-chromosome STRs (Y-STRs). Compared with length polymorphism, we observed an increase of 260 alleles (157, 31, and 72 alleles on A-STRs, X-STRs, and Y-STRs, respectively) across 36 STRs. The most substantial increments were observed in DYF387S1 and DYS389II, with increases of 287.5% and 250%, respectively. The most increment in the number of alleles was found at DYF387S1 and DYS389II (287.5% and 250%, respectively). The length-based (LB) and sequence-based (SB) combined random match probability (RMP) of 27 A-STRs were 6.05E-31 and 1.53E-34, respectively. Furthermore, other forensic parameters such as total discrimination power (TDP), cumulative probability of exclusion of trios (CPEtrio), and duos (CPEduo) were significantly improved when using the SB data, and informative data were obtained for the 94 iiSNPs. Collectively, these findings highlight the advantages of MPS technology in forensic genetics, and the Eastern Chinese Han genetic data generated in this study could be used as a valuable reference for future research in this field.

Feasibility of Human Wrist-joint Neuromuscular System Identification Method Using Functional Electrical Stimulation in Clinical Examinations

Feasibility of Human Wrist-joint Neuromuscular System Identification Method Using Functional Electrical Stimulation in Clinical Examinations

RNA-based short tandem repeat analysis on the D18S51 locus.

DNA typing based on short tandem repeat (STR) analysis is an effective forensic method for human identification. Some STRs are contained within the introns of protein-coding genes and are transcribed as pre-mRNAs. However, the possibility of using RNA for STR analysis is yet to be fully explored. Considering that RNA in forensic samples is relatively stable, especially under dry- and low-temperature conditions, we hypothesized that STR information could be obtained from RNA. Here, we investigated the possibility of conducting RNA-based STR analysis using the D18S51 locus as a model.

Sclera Segmentation and Recognition for Spectacle Dataset

Sclera, a connective tissue enveloping the eye, emerges as a novel biometric recognition method for human identification. The composition of blood vessels in the sclera proves ideal for biometric use—visible with ease, stable over time, and unique to each individual. This paper proposes sclera segmentation and recognition techniques tailored for individuals wearing spectacles. The SSV (Spectacle Sclera Vision) Dataset was meticulously created to address challenges introduced by eyewear, including reflections, distortions, and illumination variations. The study explores the unique characteristics of the sclera region, presenting a comparative analysis of traditional and neural network-based segmentation and recognition methods on the SSV Dataset. Notably, Linear SVC outperforms CNN in recognition, and UNET demonstrates superior sclera segmentation compared to OTSU. The findings provide a foundation for potential advancements in developing robust multi-class classification models for sclera biometrics in real-world scenarios. Future work involves further analysis, scalability testing, and exploration of diverse applications in ocular health and security systems.

Smart Trash Bin to Prevent Animal Disturbance Using Raspberry Pi and Deep Learning

A trash can is an indispensable component of human daily life. It is ubiquitous and extremely useful for keeping the city clean. However, littering is a frequent and widespread problem. Not only do humans litter, but so do animals, particularly monkeys. Animals are constantly searching for food, and when we discard our food waste, they are attracted and leave their natural habitats. Following that, the user may forget to lock the trash can, which may end up in the hands of the animal. With the aid of a deep learning system that detects and identifies human subjects, a smart trash bin with an autonomous locking mechanism will be used in this project to reduce animal disturbance. In this paper, Raspberry-Pi 4 module is being used. Moreover, this article describes a human subject identification method and its real-time displacement using the OpenCV library of programming roles, which is primarily aimed at Raspberry Pi with camera module. The smart trash bin system helps to reduce animal disturbance and minimise littering.

Fingerprint Door Unlock System

Abstract: Human detection and recognisation field is very significant and has undergone rapid changes with time. An important and very reliable human identification method is fingerprint identification. Fingerprint of every person. is unique. So this helps in identifying a person or in improving security of a system. Finger print of a person is read by a special type of sensor. Finger print sensor can be interfaced with a microcontroller. Through keypad we can add new user and delete the existing user, also identify the user by selecting corresponding option through keypad. In this project we use a fingerprint sensor to read ones identity to automatically operate the door of the car. For this, we use a microcontroller to enable the door opening or closing if the matching between scanned data and the already existing data is correct. Comparison is done inside the fingerprint module itself and its output is given to microcontroller. Result is displayed in a LCD display whether the user is authorized or not.LCD also helps to make troubleshooting easier. Alarming option is provided to warn about an unauthorized usage.

3D far-field Lidar sensing and computational modeling for human identification.

3D sensors offer depth sensing that may be used for task-specific data processing and computational modeling. Many existing methods for human identification using 3D depth sensors primarily focus on Kinect data, where the range is very limited. This work considers a 3D long-range Lidar sensor for far-field imaging of human subjects in 3D Lidar full motion video (FMV) of "walking" action. 3D Lidar FMV data for human subjects are used to develop computational modeling for automated human silhouette and skeleton extraction followed by subject identification. We propose a matrix completion algorithm to handle missing data in 3D FMV due to self-occlusion and occlusion from other subjects for 3D skeleton extraction. We further study the effect of noise in the 3D low resolution far-field Lidar data in human silhouette extraction performance of the model. Moreover, this work addresses challenges associated with far-field 3D Lidar including learning with a limited amount of data and low resolution. Moreover, we evaluate the proposed computational algorithm using a gallery of 10 subjects for human identification and show that our method is competitive with the state-of-the-art OpenPose and V2VPose skeleton extraction models using the same dataset for human identification.

A Deep Learning Method of Human Identification from Radar Signal for Daily Sleep Health Monitoring.

Radar signal has been shown as a promising source for human identification. In daily home sleep-monitoring scenarios, large-scale motion features may not always be practical, and the heart motion or respiration data may not be as ideal as they are in a controlled laboratory setting. Human identification from radar sequences is still a challenging task. Furthermore, there is a need to address the open-set recognition problem for radar sequences, which has not been sufficiently studied. In this paper, we propose a deep learning-based approach for human identification using radar sequences captured during sleep in a daily home-monitoring setup. To enhance robustness, we preprocess the sequences to mitigate environmental interference before employing a deep convolution neural network for human identification. We introduce a Principal Component Space feature representation to detect unknown sequences. Our method is rigorously evaluated using both a public data set and a set of experimentally acquired radar sequences. We report a labeling accuracy of 98.2% and 96.8% on average for the two data sets, respectively, which outperforms the state-of-the-art techniques. Our method excels at accurately distinguishing unknown sequences from labeled ones, with nearly 100% detection of unknown samples and minimal misclassification of labeled samples as unknown.

Development, detection and decipherment of obfuscated fingerprints in humans: Implications for forensic casework.

Fingerprints have been widely used and accepted as an effective method of human identification. This biometric tool aids in criminal investigations for personal identity for over a century. Whilst the Automatic Fingerprint Identification System (AFIS) has bolstered security efforts, it has also opened doors to potential scams, affecting both civilian and law enforcement operations. Despite extensive research on fingerprint authentication issues, very little attention has been given to addressing the problem of fingerprint alteration or obfuscation. Fraudsters, with the guidance of experts, have developed new techniques to obscure their fingerprints intentionally. Fingerprint obfuscation is the deliberate alteration of fingerprint patterns with the aim of concealing their true identity, raising concerns amongst security and investigative organizations. The objective of the current communication is to highlight the numerous techniques used for obfuscation, forgery and alteration of fingerprints in humans. It further accentuates the need for identification and interpretation of these altered fingerprints and recommends notifying law enforcement agencies of potential threats.

Gender Determination Based on CBCT Maxillary Sinus Analysis: A Systematic Review

Gender determination is an essential element for human identification in forensic medicine, to which the maxillary sinuses may contribute as they remain intact even after severe damage to the skull and other structures. Aim: To evaluate scientific evidence published over the last decade to determine whether maxillary sinus dimensions and volume may constitute useful parameters for forensic identification and gender determination, based only on cone-beam computed tomography images (CBCT). Methods: This review adhered to the PRISMA statement’s criteria. Four databases were searched for articles published between January 2010 and April 2023. Results: Initially, 1719 records were identified. After screening, there were 2475 participants in the included studies. Of the fifteen articles selected, five reported data assessing only volumetric measurements of the maxillary sinus, seven reported data calculating only linear measurements and three reported data by combining findings of both linear and volumetric measurements of the maxillary sinus. Maxillary sinus volume was significantly higher in male participants. Maxillary sinus height was the best discriminating parameter for forensic identification with an overall accuracy ranging from 70% to 80%. Conclusions: Maxillary sinus measurements revealed anatomic variability between genders, and this approach can be applied as a complementary method for human identification.

A Review of Lip Print as A Tool in Forensic Dentistry

Background: Forensic dentistry requires interdisciplinary knowledge since the data obtained from the oral cavity can contribute to identify an individual or provide the information needed in a legal process. Identification is a major problem facing forensic practitioners, DNA and fingerprints are highly useful but sometimes are not easily collected from the crime scene. Lip print could be useful in this field the lip prints are unique and distinguishable for every individual like fingerprints. The external surface of lips has many elevations and depressions forming a characteristic pattern called lip prints, examination of which is known as Cheiloscopy. However, there are many well-known implanted methods of human identification. Historical Background: The biological phenomenon of systems of grooves on the red part of human lips was first noted by anthropologists, Fischer was the first to describe it in 1902. The use of lip prints in personal identification was first recommended in France by Edmond Locard. Conclusion: This literature search covering a part of identifying a person using lip print, lip prints may also be used in detection work, being the source of criminalistic information. A lip print at the scene of a crime can be a basis for conclusions as to the character of the event, the number of the people involved, sexes, cosmetics used, habits, occupational traits, and the pathological changes of lips themselves.

EEG‐based biometric authentication system using convolutional neural network for military applications

AbstractIn this technological era, as the need for security arises, the use of biometrics is increasing in authentication systems as a secure and convenient method of human identification and verification. Electroencephalogram (EEG) signals have gained significant attention among the various biometric modalities available because of their unique and unforgeable characteristics. In this study, we have proposed an EEG‐based multi‐subject and multi‐task biometric authentication system for the military applications that address the challenges associated with multi‐task variation in EEG signals. The proposed work considers the use of respective EEG signals for the access of artillery, entrance to highly confidential places for the military and so forth by authenticated personnel only. We have used a multi‐subject, multi‐session, and multi‐task () dataset. The dataset was partially preprocessed with basic signal processing techniques such as bad channel repairing, independent component analysis for artifact removal, downsampling to 250 Hz, and an audio filter of 0.01–200 Hz for signal improvisation. This partially preprocessed dataset was further processed and was used in our deep learning model (DL) architectures. For EEG‐based biometric authentication, convolutional neural network (CNN) outperforms many of the state‐of‐the‐art DL architectures with a validation accuracy of approximately 99.86%, training accuracy of 98.49% and precision, recall and F1‐score with values of 99.91% that makes this EEG‐based approach for authentication more reliable. The DL models were also compared based on training and inference time, where CNN used the most training time but took the least time to predict the output. We compared the performance of the CNN model for three preprocessing techniques by feeding mel spectrograms, chromagrams and mel frequency cepstral coefficients, out of which mel spectrograms provided better results. This proposed architecture proves to be robust and efficient for military applications.