Human Identification Research Articles

CovertEye: Gait-Based Human Identification Under Weakly Constrained Trajectory

CovertEye: Gait-Based Human Identification Under Weakly Constrained Trajectory

Loss of the Y Chromosome: A Review of Molecular Mechanisms, Age Inference, and Implications for Men’s Health

Until a few years ago, it was believed that the gradual mosaic loss of the Y chromosome (mLOY) was a normal age-related process. However, it is now known that mLOY is associated with a wide variety of pathologies in men, such as cardiovascular diseases, neurodegenerative disorders, and many types of cancer. Nevertheless, the mechanisms that generate mLOY in men have not been studied so far. This task is of great importance because it will allow focusing on possible methods of prophylaxis or therapy for diseases associated with mLOY. On the other hand, it would allow better understanding of mLOY as a possible marker for inferring the age of male samples in cases of human identification. Due to the above, in this work, a comprehensive review of the literature was conducted, presenting the most relevant information on the possible molecular mechanisms by which mLOY is generated, as well as its implications for men’s health and its possible use as a marker to infer age.

Exploring the role of the human microbiome in forensic identification: opportunities and challenges.

Forensic microbiology is rapidly emerging as a novel tool for human identification. The human microbiome, comprising diverse microbial communities including fungi, bacteria, protozoa, and viruses, is unique to each individual, offering a new dimension to forensic investigations. While traditional identification methods primarily rely on DNA profiling and fingerprint analysis, they face limitations when complete DNA or fingerprints profiles are unattainable or degraded. In this context, the microbial signatures of the human skin microbiome present a promising alternative due to their resilience to environmental stresses and individual-specific composition. This review explores the potential of microbiome analysis in forensic human identification, evaluating its applications, advantages, limitations, and future prospects. The uniqueness of an individual's microbial community, particularly the skin microbiota, can provide distinctive biological markers for identification purposes, while technological advancements like 16S rRNA sequencing and metagenomic shotgun sequencing are enhancing the specificity of microbial identification, enabling detailed analysis of these complex ecological communities. Despite these promising findings, current research has not yet achieved a level of identification probability that could establish microbial analysis as a stand-alone evidence tool. Therefore, it is presently considered ancillary to traditional methods, contributing to a more comprehensive biological profile of individuals.

Technological Bodies: Artificial Intelligence as Friday in Portal and Halo Infinite

Artificial intelligence characters provide a unique perspective on the discussion of Friday figures and issues of Otherness and Othering as they relate to the body. With their technological, inherently inhuman physicality, AI Fridays shed light on the differentiation between ‘being human’ as an inner state of being, on the one hand, and as having a biologically human body, on the other. These characters thus shift some of the central questions about culture in many Robinsonades towards questions of humanity and physical identity, especially when reading the AI body as a metaphorical stand-in for the colonised body. They reflect on the question of how far our bodies determine who we are, how they relate to and are shaped by the groups we belong to, and what it means to be human.

Micro-Doppler based Human Activity Recognition using ABOA based Dual Spatial Convolution with Gated Recurrent Unit

The through-wall capability, device-free detection of radar-based human activity recognition are drawing a lot of interest from both academics and industry. The majority of radar-based systems do not yet combine signal analysis and feature extraction in the frequency domain and the time domain. Applications like smart homes, assisted living, and monitoring rely on human identification and activity recognition (HIAR). Radar has a number of advantages over other sensing modalities, such as the ability to shield users' privacy and conduct contactless sensing. The article introduces a new human tracking system that uses radar and a classifier called Dual Spatial Convolution Gated Recurrent Unit (DSC-GRU) to identify the subject and their behavior. The system follows the person and identifies the type of motion whenever it detects movement. One important feature is the integration of the GRU with the DSC unit, which allows the model to simultaneously capture the spatiotemporal dependence. Present prediction models just take into account spatial features that are immediately adjacent to each other, disregarding or just superimposing global spatial features when taking spatial correlation into account. A new dependency graph is created by calculating the correlation among nodes using the correlation coefficient; this graph represents the global spatial dependence, while the classic static graph represents the neighboring spatial dependence in the DSC unit. The DSC unit goes a step further by using a modified gated mechanism to quantify the various contributions of both local and global spatial correlation. While previous models performed worse, the suggested model outperformed them with an accuracy of 99.45 percent and a precision of 97.15 percent.

Ear Biometry: Protection Safeguarding Ear Acknowledgment Framework utilizing Transfer Learning in Industry 4.0

Human-Recognition using biometric features proposes a noble way of uniquely identifying individuals considering they do not require people to keep loads of passwords in mind to prove their identity. Biometrics have aided in people unable to render other’s identity as well and have advanced over the years. Identification using ear biometric technique is considered to outrun other features since passive human involvement and ease-of-access are its strong set of attributes, not seen in any other biometric techniques. It has managed to identify criminals in the Crime Branch and has various helpful applications in the industry. Even in times of  pandemic, it can serve its purpose in identifying individuals due to its feasibility. Models for Ear Image Identification have been proposed by various researchers over time utilizing Deep Learning and its Networks achieving high accuracy results, presenting faster and accurate identification models, boosting Ear Biometry as a secure Human Identification Tool. The learnings of this study on the AMI Ear Dataset and the OCEar dataset prove Ear Uniqueness of individuals demonstrating the identification of individuals, introducing passive identification into play, as well as computes on another dataset OCEar to study the similarity of both ears for identifying an individual.

Empowering artificial intelligence-based multi-biometric image sensor for human identification

Artificial intelligence (AI) and sensor technology developments have sparked revolutionary shifts in a number of fields, including biometric Identification. In order to improve human identification processes, this research offers a novel method that integrates AI and many biometric image sensors. The accuracy, robustness, and susceptibility to spoofing assaults of conventional single-modal biometric systems are among their many drawbacks. To overcome these challenges, we introduce a secure multi-biometric system that relies on feature-level fusion to identify users. In the preprocessing step, fingerprint images undergo Min-Max normalization to mitigate variations in image quality. In order to extract high-level features from both raw Electrocardiogram (ECG) signals and Min-Max normalized fingerprint images, ResNet50, a deep convolutional neural network, is used. These extracted feature vectors are able to distinguish between the two modalities. We proposed boosted Xgboost as a classifier for authentication in the identification steps to improve performance. The proposed approach is simulated using Python. A comparison study for improved Xgboost is presented using measures for accuracy, precision-recall, and F1-Score. Across all comparative metrics, the technique achieves much better performance. According to experimental findings, the suggested multi-biometric systems are more effective, dependable, and robust than the existing multi-biometric authentication systems.

Head rotation and the perception of eyelid height and contour

PurposeEffective visual perceptual processing is one of the many components of surgical competence. Human face identification is most efficient when viewed upright. However, it is not yet clear how this...

Reliable human authentication using AI-based multibiometric image sensor fusion: Assessment of performance in information security

Reliable human authentication is essential for assuring the availability, confidentiality, and integrity of sensitive data and resources in the field of information security. Due to the vulnerabilities of conventional authentication techniques like passwords and PINs, interest in biometric-based authentication systems has developed. To get over this, we suggest an efficient voice and iris recognition-based multimodal biometric verification strategy for human authenticating tools. We first collect the voice and iris datasets from 150 people. Then, we used a median filter and high pass filter to preprocess the voice and iris data. The features of voice identification are extracted using the shifted delta cepstral coefficient (SDCC) and the Mel frequency discrete wavelet coefficient (MFCC), and these two coefficients are evaluated. The outcomes of the extraction of iris identification features using Local binary pattern (LBP) and Speeded robust features (SURF) are evaluated. The classifier Fine Tuned Cuckoo Search Optimized Convolutional Neural Network (FCSO-CNN) performs voice and iris recognition modalities. Voice and iris biometric systems may be combined into a single multimodal biometric system by fusing their respective feature sets and scoring algorithms. The results of the computer simulation demonstrate that for speech recognition, using the SDC and MFC coefficients produces better results, while for iris recognition, using the LBP and FCSO-CNN experiment produces superior outcomes. Additionally, the scores fusion works superior to other scenarios in the proposed multimodal biometrics system. The suggested system provides a dependable and strong solution for human identification, assisting in the improvement of security measures across a variety of industries, including financial institutions, governmental agencies, and the defense of key infrastructure.

Age Estimation Based on Computed Tomography Analysis of the Scapula

Background and Objectives: Age estimation from skeletal remains and in living individuals is an important issue for human identification, and also plays a critical role in judicial proceedings for migrants. Forensic analysis of ossification centers is the main evaluation method for age estimation, and ossification degree can be determined using computed tomography analysis. The purpose of this study is to investigate the applicability of CT (computed tomography) in the analysis of left scapula ossification centers, for forensic age estimation in Turkish society. Materials and Methods: We analyzed six ossification centers of the left scapula and these ossification centers are the coracoid, subcoracoid, coracoid apex, acromial, glenoid, and inferior angle ossification centers. A pediatric radiologist analyzed these six ossification centers of the scapula by using a staging method defined by Schmeling et al. in 2004. Two months after the first assessment, 20 randomly selected cases was reanalyzed by the first observer and by another pediatric radiologist. Correlation between the age and ossification stage was assessed using Spearman’s nonparametric correlation test. Linear regression analysis was performed using a backwards model. Cohen’s kappa coefficient was used for evaluating interobserver and intraobserver variability. Results: In this retrospective study, 397 (248 male and 149 female) cases were evaluated. Ages ranged between 7.1 and 30.9. The mean age was 19.83 ± 6.49. We determined a positive significant correlation between the age and the ossification stages of ossification centers analyzed in both sexes. In each ossification center, except inferior angle, all of the stage 1 and 2 cases in both sexes were under 18 years old. Intraobserver and interobserver evaluations showed that reproducibility and consistency of the method was relatively good. Conclusions: The present study indicated that CT analysis of scapula ossification centers might be helpful in forensic age assessment of living individuals and dry bones.

A review of human iris identification and recognition using soft computing techniques

Computer vision and iris recognition are fields that are constantly evolving. During the past ten years, there has been a significant increase in the level of interest in computer vision, Iris recognition, soft computing techniques, neural networks, etc. This paper reviews different research papers on iris recognition. Lastly, it focuses on the international status of iris recognition.

Navigating the Boundaries of Human Identity and Artificial Intelligence in Contemporary Literature

Navigating the Boundaries of Human Identity and Artificial Intelligence in Contemporary Literature

A Review Work: Human Action Recognition in Video Surveillance Using Deep Learning Techniques

Despite being extensively used in numerous uses, precise and effective human activity identification continues to be an interesting research issue in the area of vision for computers. Currently, a lot of investigation is being done on themes like pedestrian activity recognition and ways to recognize people's movements employing depth data, 3D skeletal data, still picture data, or strategies that utilize spatiotemporal interest points. This study aims to investigate and evaluate DL approaches for detecting human activity in video. The focus has been on multiple structures for detecting human activities that use DL as their primary strategy. Based on the application, including identifying faces, emotion identification, action identification, and anomaly identification, the human occurrence forecasts are divided into four different subcategories. The literature has been carried several research based on these recognitions for predicting human behavior and activity for video surveillance applications. The state of the art of four different applications' DL techniques is contrasted. This paper also presents the application areas, scientific issues, and potential goals in the field of DL-based human behavior and activity recognition/detection.

TCN-attention-HAR: human activity recognition based on attention mechanism time convolutional network

Wearable sensors are widely used in medical applications and human–computer interaction because of their portability and powerful privacy. Human activity identification based on sensor data plays a vital role in these fields. Therefore, it is important to improve the recognition performance of different types of actions. Aiming at the problems of insufficient time-varying feature extraction and gradient explosion caused by too many network layers, a time convolution network recognition model with attention mechanism (TCN-Attention-HAR) was proposed. The model effectively recognizes and emphasizes the key feature information. The ability of extracting temporal features from TCN (temporal convolution network) is improved by using the appropriate size of the receiver domain. In addition, attention mechanisms are used to assign higher weights to important information, enabling models to learn and identify human activities more effectively. The performance of the Open Data Set (WISDM, PAMAP2 and USC-HAD) is improved by 1.13%, 1.83% and 0.51%, respectively, compared with other advanced models, these results clearly show that the network model presented in this paper has excellent recognition performance. In the knowledge distillation experiment, the parameters of student model are only about 0.1% of those of teacher model, and the accuracy of the model has been greatly improved, and in the WISDM data set, compared with the teacher's model, the accuracy is 0.14% higher.

Virtual Based Principal Leadership Model in Increasing Performance and Quality of Middle Education

Purpose: The purpose of this article is to introduce a new analytical lens for examining the role of Assisted Reproductive Technology (ART) in the construction of the human body and motherhood from a post-humanistic perspective.
  
 Method: Employing a qualitative methodology, this study conducted in-depth interviews, observations, and analysis of ART laboratory practices, utilizing samples drawn from infertility patients and ART laboratory work. The investigation delved into the spatial dynamics of ART operations within clinical settings, elucidating how technology influences the construction of the human body and the concept of motherhood.
  
 Results and conclusion: The findings reveal that ART functions not merely as a corrective measure for physical infertility but as a techno-space that reconfigures the human body to facilitate reproduction and the construction of motherhood. The spatiality enacted by ART disrupts conventional notions of human nature, engendering what is termed as techno-maternity, wherein the ontological understanding of motherhood transcends biological constraints and assumes a technological dimension.
  
 Research implications: This study underscores the imperative of adopting a post-humanistic perspective in examining the societal implications of ART. By elucidating the spatial and technological dimensions of ART's influence on human embodiment and motherhood, this research offers insights into broader socio-cultural transformations within current ages.
  
 Originality/value: This research contributes to the academic discourse by presenting a novel analytical framework that illuminates the intricate interplay between technology, embodiment, and societal constructions of motherhood. By delineating the techno-spatial dynamics of ART, this study expands the understanding of how technological interventions shape human experiences and identities in the context of reproductive health.

MiSleep: Human Sleep Posture Identification from Deep Learning Augmented Millimeter-wave Wireless Systems

In this work, we propose MiSleep , a deep learning augmented millimeter-wave (mmWave) wireless system to monitor human sleep posture by predicting the 3D location of the body joints of a person during sleep. Unlike existing vision- or wearable-based sleep monitoring systems, MiSleep is not privacy-invasive and does not require users to wear anything on their body. MiSleep leverages knowledge of human anatomical features and deep learning models to solve challenges in existing mmWave devices with low-resolution and aliased imaging and specularity in signals. MiSleep builds the model by learning the relationship between mmWave reflected signals and body postures from thousands of existing samples. Since a practical sleep also involves sudden toss-turns, which could introduce errors in posture prediction, MiSleep designs a state machine based on the reflected signals to classify the sleeping states into rest or toss-turn and predict the posture only during the rest states. We evaluate MiSleep with real data collected from Commercial-Off-The-Shelf mmWave devices for eight volunteers of diverse ages, genders, and heights performing different sleep postures . We observe that MiSleep identifies the toss-turn events start time and duration within 1.25 s and 1.7 s of the ground truth, respectively, and predicts the 3D location of body joints with a median error of 1.3 cm only and can perform even under the blankets, with accuracy on par with the existing vision-based system, unlocking the potential of mmWave systems for privacy-noninvasive at-home healthcare applications.

Психологическая непрерывность как онтологический критерий личной идентичности субъекта

In the current article the author considers the problem of the subject’s personal identity. The analysis of this concept seems to be very relevant, since in today’s public space the concept of identity is used, firstly, as non-problematic, and, secondly, as completely politicized. The analysis of the concept of identity is important because philosophy can look at the subject’s identity without prejudice and thereby depoliticize its concept. And ontology as a branch of philosophy is able to carry out a truly fundamental and comprehensive research of the concept of the subject’s identity. Personal identity turns out to be an ontological concept because the subject exists as identical to itself, and consistent reflection on the modes of existence of the subject inevitably leads us to discover the concept of its identity. The subject of the article is the subject’s personal identity. On the basis of philosophical methodology and on the method of historical analysis, the author of the study considers personal identity as something non-self-evident, placing it in the conceptual framework of subjectivity proposed by Levinas. The personal identity of the subject is endowed with the status of a mediator, smoothing out the collision of the same and the other. In this research, human identity appears as both a space and the result of a collision of familiar experience and new impressions – in the terminology of Levinas – identical and different. The temporal structure of the subject’s identity is revealed. The concept of an ontological criterion of personal identity is put forward and four historical concepts that offer such a criterion are considered: Locke, Hume, Kant and Parfit. Thus, four ontological criteria are found – consciousness, memory, transcendental criterion and psychological continuity. Each criterion is analyzed, integrated into the ontology of the subject’s personal identity and the role of each criterion is traced in the organization of its temporal structure. The results of the research are the reconstruction of the discovered concepts in the status of ontological criteria of personal identity, their comparative analysis is carried out. It is concluded that Parfit’s psychological continuity is the most consistent of all the analyzed criteria because it simultaneously affirms the possibility of the subject’s personal identity and outlines the boundaries of the application of this concept. Though psychological continuity doesn’t fit in Levinas’ conceptual frame because it doesn’t solve existential and ontological problems, that exist around concept of personal identity.

The emotional impact of generative AI: negative emotions and perception of threat

ABSTRACT Generative Artificial Intelligence (AI) is a rapidly expanding field that aims to develop machines capable of performing tasks that were previously considered unique to humans, such as learning, reasoning, problem-solving, and decision-making. The recent release of several tools based on AI (e.g. ChatGPT) has sparked debates on the potential of this technology and garnered widespread attention in the mainstream media. Using a socio-psychological approach, in three studies (total N = 410), we demonstrate that when faced with Generative AI’s ability to reproduce the complexity of human cognitive capabilities, participants reported significantly higher negative emotions than those in the control group. In turn, negative emotions elicited by a specific type of AI (e.g. generative AI) were associated to the perception of threat extended to AI technologies as a whole, understood as a threat to various aspects of human life, including jobs, resources, identity, uniqueness, and value. Our findings emphasise the importance of considering emotional and societal impacts when developing and deploying advanced AI technologies and implementing responsible guidelines to minimise adverse effects. As AI technology advances, addressing public concerns and regulating its usage is crucial for the benefit of society. To achieve this goal, collaboration between experts, policymakers, and the public is necessary.

Preserving Linguistic Diversity in the Digital Age: A Scalable Model for Cultural Heritage Continuity

In the face of the rapid erosion of both tangible and intangible cultural heritage globally, the urgency for effective, wide-ranging preservation methods has never been greater. Traditional approaches in cultural preservation often focus narrowly on specific niches, overlooking the broader cultural tapestry, particularly the preservation of everyday cultural elements. This article addresses this critical gap by advocating for a comprehensive, scalable model for cultural preservation that leverages machine learning and big data analytics. This model aims to document and archive a diverse range of cultural artifacts, encompassing both extraordinary and mundane aspects of heritage. A central issue highlighted in the article is the dominance of English, especially in popular culture, and its role in reshaping cognitive patterns, leading to the loss of traditional languages and cultures. This trend, favoring orality over literacy, amplifies the need for the preservation actions proposed. The article underscores the importance of establishing a more meaningful and comprehensive lingua franca as the first step in this endeavor. This approach is not just about safeguarding languages but also about preserving the nuanced ways in which different linguistic paradigms shape our understanding of the world. The paper explores how language evolution, translation nuances, and the unique perspectives inherent in various linguistic paradigms contribute to a rich cultural diversity. It also examines the impact of technology and social media on traditional cultural norms and the critical need for deliberate efforts to reinforce endangered languages and practices. The proposed model aims to bridge the gap between past and present cultural expressions, using technology to reinforce mother-tongue contexts and enhance the appreciation of diverse human identities and worldviews. Through this, the article presents a compelling case for a more inclusive and holistic approach to preserving the global tapestry of cultural heritage.

Deep learning-based multi-head self-attention model for human epilepsy identification from EEG signal for biomedical traits

Deep learning-based multi-head self-attention model for human epilepsy identification from EEG signal for biomedical traits