Person Recognition Research Articles

Face-voice based multimodal biometric authentication system via FaceNet and GMM.

Information security has become an inseparable aspect of the field of information technology as a result of advancements in the industry. Authentication is crucial when it comes to dealing with security. A user must be identified using biometrics based on certain physiological and behavioral markers. To validate or establish the identification of an individual requesting their services, a variety of systems require trustworthy personal recognition schemes. The goal of such systems is to ensure that the offered services are only accessible by authorized users and not by others. This case study provides enhanced accuracy for multimodal biometric authentication based on voice and face hence, reducing the equal error rate. The proposed scheme utilizes the Gaussian mixture model for voice recognition, FaceNet model for face recognition and score level fusion to determine the identity of the user. The results reveal that the proposed scheme has the lowest equal error rate in comparison to the previous work.

Personality recognition in Digital Humanities: A review of computational approaches in the humanities

Abstract One of the most fascinating aspects of human beings is their personality. Two models that are currently being researched and widely used in computational approaches are the Myers–Briggs Type Indicator and the Big Five (or OCEAN). In this study, we will briefly examine the history of these two models and the current state of their applications in the Digital Humanities field. Although categorizing research in Digital Humanities is a challenging task, we have chosen to include works that, while primarily psychological in nature, use methodologies and methods from Digital Humanities, specifically in literary texts. Consequently, we can divide this research into two categories. On the one hand, there are works that aim to study and identify the personalities of fictional characters in literature or movies. On the other hand, there are works that aim to recreate personalities in virtual characters based on a predetermined model. We will therefore examine the works proposed by the scientific community for both approaches.

The rights of Nature: the legal revolution of the 21st century

The article tries to show how the ecocentric approach, which preaches Ecological Justice, finds full legal recognition today both in Jurisprudence and in Law. Three cases will be studied where Nature is recognized as a subject of law, that is, environmental entities, such as rivers or lagoon, are granted recognition as subjects of law with their own rights, which generates protection and restoration obligations. The first, the Atrato River in Colombia and the second, the Whanganui River in New Zealand. The study of these cases gave me the possibility of fighting for the rights of the Mar Menor, which is the third case study of this work: the Laguna del Mar Menor in Spain, Law 19/2022, of September 30, of recognition of legal personality to the Mar Menor Lagoon and its basin.

DesPrompt: Personality-descriptive prompt tuning for few-shot personality recognition

Personality recognition in text is a critical problem in classifying personality traits from the input content of users. Recent studies address this issue by fine-tuning pre-trained language models (PLMs) with additional classification heads. However, the classification heads are often insufficiently trained when annotated data is scarce, resulting in poor recognition performance. To this end, we propose DesPrompt to tune PLM through personality-descriptive prompts for few-shot personality recognition, without introducing additional parameters. DesPrompt is based on the lexical hypothesis of personality, which suggests that personalities are revealed by descriptive adjectives. Specifically, DesPrompt models personality recognition as a word-filling task. The input content is first encapsulated with personality-descriptive prompts. Then, the PLM is supervised to fill in the prompts with label words describing personality traits. The label words are selected from trait-descriptive adjectives from psychology findings and lexical knowledge. Finally, the label words filled in by PLM are mapped into the personality labels for recognition. Our approach aligns with the Masked Language Modeling (MLM) task in pre-training PLMs. So, it efficiently utilizes pre-trained parameters to reduce dependence on annotated data. Experiments on four public datasets show that DesPrompt outperforms conventional fine-tuning and other prompt-based methods, especially in zero-shot and few-shot settings.

Classification of Ethnicity Using Efficient CNN Models on MORPH and FERET Datasets Based on Face Biometrics

Ethnic conflicts frequently lead to violations of human rights, such as genocide and crimes against humanity, as well as economic collapse, governmental failure, environmental problems, and massive influxes of refugees. Many innocent people suffer as a result of violent ethnic conflict. People’s ethnicity can pose a threat to their safety. There have been many studies on the topic of how to categorize people by race. Until recently, the majority of the work on face biometrics had been conducted on the problem of person recognition from a photograph. However, other softer biometrics such as a person’s age, gender, race, or emotional state are also crucial. The subject of ethnic classification has many potential uses and is developing rapidly. This study summarizes recent advances in ethnicity categorization by utilizing efficient models of convolutional neural networks (CNNs) and focusing on the central portion of the face alone. This article contrasts the results of two distinct CNN models. To put the suggested models through their paces, the study employed holdout testing on the MORPH and FERET datasets. It is essential to remember that this study’s results were generated by focusing on the face’s central region alone, which saved both time and effort. Classification into four classes was achieved with an accuracy of 85% using Model A and 86% using Model B. Consequently, classifying people according to their ethnicity as a fundamental part of the video surveillance systems used at checkpoints is an excellent concept. This categorization statement may also be helpful for picture-search queries.

Research on the application of artificial intelligence to auto-driving cars

Since the concept of artificial intelligence was introduced in the 1950s, artificial intelligence technologies have emerged more and more frequently. Besides, the application of artificial intelligence technologies in driverless cars has increased, especially in the field of image recognition. Many deep learning methods of image recognition have emerged in the field of autonomous driving. The primary purpose of this paper is to highlight the importance and functionality of using neural networks and image recognition in artificial intelligence for driverless functions. This paper also provides a comprehensive review of the core aspects of driverless technology, the role of image recognition and person recognition in environment perception, the impact of deep learning on driverless behavior decisions, and driverless vehicle kinetic energy control systems. Although the driverless car technology is now being updated, all three modes have great accuracy and results. However, each subsystem still faces many problems and challenges, which may be solved in future research. The driverless car technology would be promoted on a large scale after its safety and suitability are ensured.

The COVERED FACE: FaceNet-based partially occluded person recognition - on attention mechanism

This paper investigates the problem of improving the recognition accuracy of partially occluded faces. We aim to insert the attention mechanism blocks into the FaceNet to improve its recognition capability. FaceNet has excellent performance as a general-purpose face recognition system. However, recognizing faces with incomplete facial features is a challenge for feature extraction networks. To overcome this weakness and improve the recognition range of FaceNet, this paper proposes to incorporate the attention mechanism in the feature extraction network so that the attention of the model will be focused on the face features that are not occluded. The addition of the attention mechanism makes the application scenario of FaceNet more flexible and improves the anti-interference ability and robustness of the model. The results show that FaceNet trained by non-specific target dataset can improve the performance after adding the attention mechanism. In detail, the improvement in recognition for regular datasets is small. Nevertheless, the performance of the model is significantly improved for certain datasets with extreme conditions, such as face images with occlusions. In general, inserting the attention mechanism blocks in FaceNet can improve its value and expand its usage environment.

Application of Foot Hallux Contact Force Signal for Assistive Hand Fine Control.

Accurate recognition of disabled persons' behavioral intentions is the key to reconstructing hand function. Their intentions can be understood to some extent by electromyography (EMG), electroencephalogram (EEG), and arm movements, but they are not reliable enough to be generally accepted. In this paper, characteristics of foot contact force signals are investigated, and a method of expressing grasping intentions based on hallux (big toe) touch sense is proposed. First, force signals acquisition methods and devices are investigated and designed. By analyzing characteristics of signals in different areas of the foot, the hallux is selected. The peak number and other characteristic parameters are used to characterize signals, which can significantly express grasping intentions. Second, considering complex and fine tasks of the assistive hand, a posture control method is proposed. Based on this, many human-in-the-loop experiments are conducted using human-computer interaction methods. The results showed that people with hand disabilities could accurately express their grasping intentions through their toes, and could accurately grasp objects of different sizes, shapes, and hardness using their feet. The accuracy of the action completion for single-handed and double-handed disabled individuals was 99% and 98%, respectively. This proves that the method of using toe tactile sensation for assisting disabled individuals in hand control can help them complete daily fine motor activities. The method is easily acceptable in terms of reliability, unobtrusiveness, and aesthetics.

Attendance System using Haar Cascade Approach

Abstract: The participation framework is utilized to track and screen whether an understudy goes to a class. There are various kinds of participation frameworks like Biometric-based, Radio recurrence card-based, face acknowledgment based and old paper-based participation framework. Out of them every one of the, a Face acknowledgment based participation framework is safer and efficient. There are a few examination papers zeroing in on just the acknowledgment pace of understudies.. This research focusing on a face recognition based attendance system with getting a less false-positive rate using a threshold to confidence We used Haar cascade for face detection of their strength and LBPH calculation for face acknowledgment. It is hearty against monotonic dark scale changes. Situations, for example, face acknowledgment rate, misleading positive rate for that and bogus positive rate with and without involving an edge in recognizing obscure people are considered to assess our framework. We got face acknowledgment pace of understudies is 77% and its bogus positive rate is 28%. This system is seeing students regardless, when students are wearing glasses or grown a beard development . Face Recognition of unknown persons is nearly 60% for both with and without applying threshold value. Its bogus positive rate is 14% and 30% with and without applying edge separately.

예비전문상담교사의 교육실습 경험에 대한 내용분석

Purpose: The purpose of this study was to identify the educational practice experience of prospectiveprofessional counseling teachers and to explore ways to improve their educational practice experience. Methods: To achieve the objectives of the study, a Focus Group Interview(FGI) was conducted forprospective professional counseling teachers who had completed teaching courses in the bachelor'sor master's programs. The data collected through interviews were analyzed according to Krippendorff's(2013) qualitative content analysis procedures and methods. Results: As a result of the analysis, eighteen semantic contents, nine categories, and three dimensionswere identified. The three dimensions were the ‘individual dimension,’ the ‘relational dimension’ and the‘administrative dimension.’ For the individual dimension, the categories of ‘giving various meanings toeducational practice,’ ‘career planning preparation,’ and ‘personal meaning recognition’ were derived,and at the relational dimension, the categories of ‘conflicting experiences of student guidance,’ ‘perceptionof educational practice instructors’ and ‘experiences with peer trainee’ were derived. At the administrativedimension, the categories of ‘desire for various matters of educational practice,’ ‘guidance content ofvarious aspects of educational practice,’ and ‘perception of institutional improvement and supplementation’were derived. Conclusion: Results of this study suggest that the role of educational practice instructors is importantand that it is necessary to reflect on the specificity of school counseling to educational practice. Basedon the results, this study suggested what should be considered in the educational practice of prospectiveprofessional counseling teachers.

The Relationship between Transformation Leadership, Job Satisfaction and Employee Motivation in the Tourism Industry

The objective of this study is to investigate the relationship between transformation leadership, job satisfaction, and employee motivation in the Vietnam tourism industry (VTI). In the study, data were collected from 207 respondents from different organizations related to the tourism industry, then partial least squares structural equation modeling (PLS-SEM) was employed for the analysis. The findings indicate that the paths between inspirational communication, personal recognition, supportive leadership, vision, and job satisfaction are highly significant. However, the intellectual stimulation does not positively affect job satisfaction. In addition, the path of job satisfaction and employee motivation is significant. This research also shows that there is only supportive leadership has a direct positive effect on employee motivation. The findings can be used as reference for tourism company managers to consult and improve their knowledge, skills, and leadership qualities.

How do social entrepreneurs compete? Entrepreneurial legitimacy in the Silicon Valley of humanitarian innovations

Social entrepreneurship has attracted much attention from celebrities, scholars, policymakers, and wealthy foundations. Yet little is known about this form of entrepreneurship beyond what is said in mission statements, autobiographies, and heroic narratives of social entrepreneurs. This paper contributes to this literature by discussing a feature of social entrepreneurship largely neglected by researchers: competition. Drawing on an ethnographic analysis inspired by Bourdieu, I argue that social entrepreneurs may not only compete for funds but also for legitimacy derived from resources such as personal recognition and economic capital.

Research of the Characteristics of a Convolutional Neural Network on the ESP32-CAM Microcontroller

The paper is devoted to solving the problem of using neural networks for real-time image recognition on low-power portable devices running on microcontrollers. The ESP-32© CAM microcontroller was used as the target device, on which an artificial neural network was deployed, written using the Python® programming language and the Tensorflow© library for building neural networks. The performance of the microcontroller and personal computer for object detection using a neural network and their classification were compared in the paper. The image recognition time and percentage of correctly classified objects were compared. The paper shows that the number of training epochs affects the accuracy of object classification in the image. The obtained results show that increasing the number of training epochs increases the accuracy of object recognition using the studied neural network, but a significant increase in the number of epochs does not lead to a significant improvement in recognition accuracy. The difference in the obtained results for the microcontroller and personal computer image recognition accuracy ranges from 5%.

Person Recognition Based on Deep Gait: A Survey.

Gait recognition, also known as walking pattern recognition, has expressed deep interest in the computer vision and biometrics community due to its potential to identify individuals from a distance. It has attracted increasing attention due to its potential applications and non-invasive nature. Since 2014, deep learning approaches have shown promising results in gait recognition by automatically extracting features. However, recognizing gait accurately is challenging due to the covariate factors, complexity and variability of environments, and human body representations. This paper provides a comprehensive overview of the advancements made in this field along with the challenges and limitations associated with deep learning methods. For that, it initially examines the various gait datasets used in the literature review and analyzes the performance of state-of-the-art techniques. After that, a taxonomy of deep learning methods is presented to characterize and organize the research landscape in this field. Furthermore, the taxonomy highlights the basic limitations of deep learning methods in the context of gait recognition. The paper is concluded by focusing on the present challenges and suggesting several research directions to improve the performance of gait recognition in the future.

Health consequences of exposure to aircraft contaminated air and fume events: a narrative review and medical protocol for the investigation of exposed aircrew and passengers

Thermally degraded engine oil and hydraulic fluid fumes contaminating aircraft cabin air conditioning systems have been well documented since the 1950s. Whilst organophosphates have been the main subject of interest, oil and hydraulic fumes in the air supply also contain ultrafine particles, numerous volatile organic hydrocarbons and thermally degraded products. We review the literature on the effects of fume events on aircrew health. Inhalation of these potentially toxic fumes is increasingly recognised to cause acute and long-term neurological, respiratory, cardiological and other symptoms. Cumulative exposure to regular small doses of toxic fumes is potentially damaging to health and may be exacerbated by a single higher-level exposure. Assessment is complex because of the limitations of considering the toxicity of individual substances in complex heated mixtures.There is a need for a systematic and consistent approach to diagnosis and treatment of persons who have been exposed to toxic fumes in aircraft cabins. The medical protocol presented in this paper has been written by internationally recognised experts and presents a consensus approach to the recognition, investigation and management of persons suffering from the toxic effects of inhaling thermally degraded engine oil and other fluids contaminating the air conditioning systems in aircraft, and includes actions and investigations for in-flight, immediately post-flight and late subsequent follow up.

Portrait and Mugshot: Metonymical Foundation of Photographic Genres

This article focuses on the genre distinction between artistic and legal photographs of faces: while the artistic portrait tends to express the singular soul of the person pictured, the biometric mugshot aims to scan singular physical traits without any psychological expression. How do these photographic genres allow us to identify the represented person? What do each of them seek to recognize? To grasp our metaphysical expectations of photographic technology, and thus to bridge the gap between discursive styles of these two photographic genres, I revise Derrida’s deconstruction of the law of genre. Further, I argue that Derrida’s and Nancy’s subversive readings of Kant’s concepts of parergon and schema help us to understand the rhetorical setting of the human mind, which organizes the photographic work of framing fragments. Finally, I explain the metaphysical conditions of possibility for both photographic genres by situating their opposite goals in the interval of personal recognition constructed by metonymical schematism.

MLP Tabanlı DNN Modeli Kullanılarak Akıllı Alanlar İçin Yürüyüş Analizinden Kişi Tanıma

In smart fields, security measures are taken to protect people against threats that may arise by using technology and to provide crisis management, and the functions of measuring area security and ensuring its effectiveness are carried out. As an element of this measurement, it is thought that person recognition may be the most important factor in the future. It is seen that deep learning-based algorithms, which can provide fast and high-accuracy results with many data, will be an integral part of this sector in the future as they are today. However, when the literature is examined, it is understood that the number of research in which Deep learning algorithms are used in order to increase the success of the studies in this direction and the system practicality is insufficient. For this reason, in this study, deep learning was used to recognize people by using the walking data of 15 people obtained thanks to wearable sensors. Since the increase in the diversity of the data will positively affect the learning of the created model, data augmentation has been made and these data have been classified in the MLP-based DNN model. The results were statistically analyzed and showed that this model exhibited excellent performance in person recognition from walking data. In addition, the ACC rate was found to be 100%, and it proved that the method used to increase the data also produced successful results in walking data. It is thought that the success of the study can provide important perspective support to new studies for smart fields in the literature.

출연방식 자회사형 장애인표준사업장 도입 방안에 대한 연구

The purpose of this study is to introduce the corporate-funded subsidiary type standard workplace for the disabled. We developed a delphi survey questionnaire through the literature review related to the standard workplace for the disabled and the opinions from experts and related ministries. A total of 30 individuals participated in the delphi survey and it was conducted twice. Participants consisted of 10 professors and researchers, 5 experts in persons with disabilities employment institutions, 8 experts in standard workplace for the disabled, and 7 experts in the field of rehabilitation counseling. After conducting the delphi survey, mean and median score, standard deviation, interquartile range, and content validity ratio (CVR) were analyzed. Based on results, we suggested implications for the establishment requirements, percentage of employment recognition for persons with disabilities, and operational and management.

Selection of the Minimum Number of EEG Sensors to Guarantee Biometric Identification of Individuals.

Biometric identification uses person recognition techniques based on the extraction of some of their physical or biological properties, which make it possible to characterize and differentiate one person from another and provide irreplaceable and critical information that is suitable for application in security systems. The extraction of information from the electrical biosignal of the human brain has received a great deal of attention in recent years. Analysis of EEG signals has been widely used over the last century in medicine and as a basis for brain-machine interfaces (BMIs). In addition, the application of EEG signals for biometric recognition has recently been demonstrated. In this context, EEG-based biometric systems are often considered in two different applications: identification (one-to-many classification) and authentication (one-to-one or true/false classification). In this article, we establish a methodology for selecting and reducing the minimum number of EEG sensors necessary to carry out effective biometric identification of individuals. Two methodologies were applied, one based on principal component analysis and the other on the Wilcoxon signed-rank test in order to reduce the number of electrodes. This allowed us to identify, according to the methodology used, the areas of the cerebral cortex that would allow selection of the minimum number of electrodes necessary for the identification of individuals. The methodologies were applied to two databases, one with 13 people with self-collected recordings using low-cost EEG equipment, EMOTIV EPOC+, and another publicly available database with recordings from 109 people provided by the PhysioNet BCI.

Optimal Score Level Fusion for Multi-Modal Biometric System with Optimised Deep Ensemble Technique

ABSTRACT Multi-modal biometric refers to the use of various biometric indicators for individual identification by personal recognition systems. When compared to unimodal biometrics, which uses only one biometric data, such as a fingerprint, face, palm print or iris, multi-modal authentication offers a higher degree of authentication. A new optimal score value that fuses deep learning and multi-modal biometrics would be produced in the project study. A proposed approach was split into three main groups: feature extraction, pre-processing and ensemble recognition. First, median filtering and ROI procedures were utilised for pre-processing-captured original biometric information for the wrist, dorsal, palm vein and palm print. Pertinent features are then retrieved from the corresponding preprocessed images for every modality. These extracted features are subjected to an imposter or genuine determination. Neural Network 1, Neural Network 2 and Deep Convolution Neural Network create a new deep ensemble model in the event of a forgery or accurate estimation (DCNN). The outputs of NN1 and NN2 are the inputs to DCNN, which provides information on whether the biometric data are authentic or not. Finally, the results are fine-tuned by the weight of DCNN utilising new hybrid optimisation scheme referred as Butterfly combined Tunicate Swarm Optimisation (BTSA).