Recognition Technology Research Articles

Advancements in Fingerprint Recognition Through Deep Learning: A Comprehensive Analysis of Novel Algorithms

Abstract. This paper presents a comprehensive exploration of the application of deep learning technology in fingerprint recognition, focusing on the development and impact of several novel algorithms. These algorithms have been specifically designed to address challenges in key sub-fields such as pose estimation, direction field estimation, minutiae extraction, and minutiae matching. By integrating deep learning techniques, these new approaches significantly enhance the accuracy, stability, and efficiency of fingerprint recognition systems. The study demonstrates that these algorithms surpass traditional methods in several critical areas, offering improved precision in recognizing fingerprints, particularly in high-noise environments. Furthermore, the fully differentiable nature of these models contributes to their robustness, enabling more consistent and reliable performance across diverse scenarios. The results underscore the potential for these deep learning-based algorithms to set new benchmarks in the field, with broad implications for their application in security, law enforcement, and other areas requiring reliable biometric authentication. The use of these cutting-edge methods is anticipated to be vital in influencing the direction of fingerprint recognition technology as it develops further, guaranteeing increased security and precision.

Unveiling user identity across social media: a novel unsupervised gradient semantic model for accurate and efficient user alignment

AbstractThe field of social network analysis has identified User Alignment (UA) as a crucial area of investigation. The objective of UA is to identify and connect user accounts across diverse social networks, even when there are no explicit interconnections. UA plays a pivotal role in synthesising coherent user profiles and delving into the intricacies of user behaviour across platforms. However, traditional approaches have encountered limitations. Singular embedding techniques have been found to fall short in fully capturing the semantic essence of user profile attributes. Furthermore, classification-based embedding methods lack definitive criteria for categorisation, thereby constraining both the efficacy and applicability of these models. This paper presents a novel unsupervised Gradient Semantic Model for User Alignment (GSMUA) for the purpose of identifying common user identities across social networks. GSMUA categorises user profile information into weak, sub, and strong gradients based on the semantic intensity of attributes. Different gradient semantic levels direct attention to literal features, semantic features, or a combination of both during feature extraction, thereby achieving a full semantic representation of user attributes. In the case of strongly semantic long texts, GSMUA employs Named Entity Recognition (ENR) technology in order to enhance the inefficient handling of such texts. Furthermore, GSMUA compensates for missing user profile attributes by utilising profile information from user neighbours, thereby reducing the negative impact of missing user profile attributes on model performance. Extensive experiments conducted on four pairs of real datasets demonstrate the superiority of our approach. In comparison to the most effective previously developed unsupervised methods, GSMUA demonstrates improvements in hit-precision ranging from 5.32 to 12.17%. When compared to supervised methods, the improvements range from 0.71 to 11.79%.

Implementing Facial Recognition Technology in a University Setting: An Organizational Justice Perspective

Facial recognition technology (FRT) has become widespread in society, and its adoption in organizational settings is viewed as a natural continuation of established surveillance practices. However, concerns and resistance to FRT often arise due to controversial applications and improper use. This study contributes to the understanding of FRT in organizational contexts by conducting semi-structured interviews in a university that illustrates the larger themes of organizational justice and the ethical complexities surrounding the deployment of this technology. Drawing on organizational justice theory, we analyze users’ attitudes towards FRT deployment, their perceptions of organizational justice, and if and how the deployment adhered to or violated organizational justice principles. Our study revealed that this implementation case violated all aspects of organizational justice. Users expressed concerns regarding face capture, potential discrimination, authoritarian practices, and harm to community norms. We also provide recommendations to address organizational justice issues associated with implementing new, controversial technologies, considering technology acceptability perspectives.

IOT Gesture Recognition-Based Virtual Keyboard and Mouse

Recognition-based virtual mouse and keyboard systems use gesture recognition technology to control mouse movements and keyboard inputs without physical devices. Instead of using a physical mouse or keyboard, users can perform hand or finger gestures in the air, which are captured and interpreted by a camera or sensor system. Gesture abstracts in this context refer to the specific hand or finger movements that are recognized and associated with certain mouse or keyboard actions. These gestures are predefined and programmed into the recognition system, allowing users to perform specific movements to simulate the actions of a traditional mouse or keyboard

Analysis and Application of Particle Backtracking Algorithm in Wind–Sand Two-Phase Flow Using SPH Method

Due to the high sensitivity of grid-based micro-scale wind–sand flow models to deformation and distortion, this study employs the Smooth Particle Hydrodynamics (SPH) method for numerical simulations. The advantage of the SPH method is that it can dynamically analyze the entire trajectory of the particles, thus allowing the initial positional distribution of sand-buried particles to be traced. This study utilizes the advantages of the SPH method. It develops particle backtracking algorithms based on the SPH method using the C language. It analyses the initial location distribution, concentration, velocity, and particle size distribution of sand-buried particles to formulate targeted measures to cope with wind–sand disasters. Meanwhile, this paper improves a particle modeling algorithm to realize arbitrary mixing particle size and mixing ratio by programming in C language and combining it with pixel recognition technology. In addition, this paper will use the particle backtracking algorithm to analyze the classical embankment wind and sand flow field and then propose adequate measures for embankment wind and sand disaster management by investigating sand particle movement characteristics.

SEGURANÇA PÚBLICA, VIGILANTISMO E NOVAS TECNOLOGIAS: O RECONHECIMENTO FACIAL COMO FERRAMENTA DE GARANTIA DE DIREITOS

This study analyzes the use of facial recognition technologies as a public security tool, addressing its benefits and associated ethical and legal challenges. The research highlights that, although the technology can improve efficiency in identifying suspects and solving crimes, especially during large events, significant concerns remain regarding privacy and discrimination. In Brazil, inadequate implementation and the lack of specific regulations reinforce criticisms of the unequal application of the technology, particularly in vulnerable communities. The study suggests that public acceptance of facial recognition depends on transparency, proper oversight, and alignment with legal frameworks, such as the LGPD. Only through clear and ethical governance will it be possible to balance the benefits of public security with the protection of fundamental rights.

SURVEY ON AI BASED DOG BREED PREDICTION MODEL AND VIRTUAL CANINE HUB

Abstract—This research focuses on the difficulties that dog enthusiasts face when trying to accurately and efficiently identify different canine breeds. Recognizing the challenges of obtaining breed information di- rectly from dog owners, a new solution is proposed: an automated system that can be accessed through a user-friendly website. This system utilizes image recognition technology, allowing users to easily upload a photo of a dog and accurately determine its breed. In addition to breed identifica- tion, the platform offers comprehensive information about temperament, care requirements, and other unique characteristics of each breed, pro- viding a holistic understanding. The objective is to empower potential dog owners to make well-informed decisions that align with theirlifestyle and preferences. Specifically tailored for the United States market and available in English, this resource aims to assist dog lovers nationwide in selecting and caring for their pets by providing detailed and easily - accessible information. Keywords: User-friendly, Temperament, Holistic

Replay-Based Incremental Learning Framework for Gesture Recognition Overcoming the Time-Varying Characteristics of sEMG Signals

Gesture recognition techniques based on surface electromyography (sEMG) signals face instability problems caused by electrode displacement and the time-varying characteristics of the signals in cross-time applications. This study proposes an incremental learning framework based on densely connected convolutional networks (DenseNet) to capture non-synchronous data features and overcome catastrophic forgetting by constructing replay datasets that store data with different time spans and jointly participate in model training. The results show that, after multiple increments, the framework achieves an average recognition rate of 96.5% from eight subjects, which is significantly better than that of cross-day analysis. The density-based spatial clustering of applications with noise (DBSCAN) algorithm is utilized to select representative samples to update the replayed dataset, achieving a 93.7% recognition rate with fewer samples, which is better than the other three conventional sample selection methods. In addition, a comparison of full dataset training with incremental learning training demonstrates that the framework improves the recognition rate by nearly 1%, exhibits better recognition performance, significantly shortens the training time, reduces the cost of model updating and iteration, and is more suitable for practical applications. This study also investigates the use of the incremental learning of action classes, achieving an average recognition rate of 88.6%, which facilitates the supplementation of action types according to the demand, and further improves the application value of the action pattern recognition technology based on sEMG signals.

Optimization Research on Integrating Mental Health Into Ideological and Political Theory Courses Through Deep Learning Network Applications

This study aims to evaluate the effectiveness of incorporating mental health education into ideological and political theory classrooms using speech emotion recognition technology from the field of human-computer interaction. Mental health education is essential for students' well-being, and this study establishes a theoretical framework for its integration into the curriculum. Utilizing recurrent neural networks (RNNs) augmented with attention mechanisms, the research demonstrates the model's proficiency in identifying subtle emotional cues within speech data. The model achieves an average recognition accuracy of 87.21% on the RAVDESS speech emotion corpus, showing particular strengths in detecting emotions like happiness and boredom. The findings suggest that this technology can be effectively employed to gauge student satisfaction with different teaching methodologies post-integration, offering valuable insights for refining educational practices in ideological and political theory classes through improved mental health education initiatives.

Research on image recognition and processing application technology of unmanned vehicle based on deep learning

Abstract. Image recognition technology is critically important in various fields, including the rapidly advancing sector of autonomous vehicles. As one of the core components enabling driverless cars to perceive their environment and make informed decisions, image recognition has seen significant advancements due to deep learning. This paper focuses on the application of deep learning in image recognition for self-driving cars and explores its implications for the future of autonomous driving technology. To begin with, this paper examines the empirical evaluation of deep learning models in highway driving scenarios. By employing Convolutional Neural Networks (CNN), these models achieve high detection rates and superior accuracy in recognizing vehicles and lanes. The robustness of these models is tested under varying weather conditions and times, demonstrating their effectiveness compared to classical computer vision techniques. Next, the paper discusses radar-camera fusion technology, highlighting different fusion strategies such as data-level, feature-level, object-level, and hybrid-level. The findings suggest that while feature-level fusion excels in detecting small objects in complex scenes, hybrid-level fusion is optimal for diverse driving situations. This section provides valuable insights into the integration of multimodal data for improved object recognition and semantic segmentation. After discussing fusion technologies, the paper finally reviews the security challenges posed by adversarial attacks on deep learning-based unmanned systems.

A Comprehensive Analysis of Face Recognition Based on Advanced Deep Learning Technology

Abstract. Facial recognition technology is becoming increasingly important in many fields due to its non-invasiveness and rapid improvements in accuracy. This study provides a comprehensive review of face recognition methods, covering both traditional and modern approaches. The key models such as feature face, Fischer face, Visual Geometry Group face (VGG-Face), FaceNet and ArcFace are analyzed, and the complete process from data preprocessing to feature extraction and model training is described in detail. By using the Wild Labeled Face Dataset (LFW) and the Network Face Dataset of the Institute of Automation of the Chinese Academy of Sciences (CASIA-WebFace) for experiments, the research found that although the deep learning model has high accuracy, it also faces challenges such as data dependence and large computing requirements. At the same time, the research highlights the issues of fairness and bias in these models, providing directions for future research and contributing to the sustainable and ethical development of face recognition technology.

The Latest Development of Synthetic Aperture Radar: An Overview

Abstract. Synthetic Aperture Radar (SAR) technology enables high-resolution imaging regardless of weather conditions, offering immense potential for target detection and recognition. This paper reviews the latest advancements in SAR technology, highlighting key developments and applications. The limitations of traditional SAR imaging techniques, particularly the trade-off between resolution and swath width, have been addressed by High Resolution Wide Swath (HRWS) SAR, enabling simultaneous acquisition of high-resolution and wide-swath data. Multi-polarimetric SAR provides richer scattering information for improved target recognition accuracy, while deep learning algorithms enhance image quality and recognition performance. Emerging technologies like 3D-ISAR and holographic imaging offer novel approaches to target recognition and tracking. We discuss the development history, performance advantages, and limitations of these technologies, providing a comprehensive overview of their practical applications. We identify current challenges and future trends in SAR target imaging and recognition, including computational efficiency, adversarial attack defense, data acquisition and processing, and feature extraction and classification. Potential research directions, such as integrated multi-mode SAR systems, multi-source data fusion, and adversarial attack defense strategies, are outlined to fully harness the potential of SAR technology in target imaging and recognition.

CartoMark: a benchmark dataset for map pattern recognition and map content retrieval with machine intelligence

Maps are fundamental medium to visualize and represent the real word in a simple and philosophical way. The emergence of the big data tide has made a proportion of maps generated from multiple sources, significantly enriching the dimensions and perspectives for understanding the characteristics of the real world. However, a majority of these map datasets remain undiscovered, unacquired and ineffectively used, which arises from the lack of numerous well-labelled benchmark datasets, which are of significance to implement the deep learning techniques into identifying complicated map content. To address this issue, we develop a large-scale benchmark dataset involving well-labelled datasets to employ the state-of-the-art machine intelligence technologies for map text annotation recognition, map scene classification, map super-resolution reconstruction, and map style transferring. Furthermore, these well-labelled datasets would facilitate map feature detection, map pattern recognition and map content retrieval. We hope our efforts would provide well-labelled data resources for advancing the ability to recognize and discover valuable map content.

Advancements in Image Recognition: Comparing CNNs and Vision Transformers

Abstract. This paper explores advancements in image recognition technologies, highlighting the shift from conventional methodologies to contemporary deep learning techniques, specifically focusing on Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). The study examines key architectures including CNNs, and various Transformer-based models, analyzing their performance evaluating their effectiveness in diverse tasks such as image classification, object detection, and facial recognition. The research highlights the strengths and limitations of CNNs and ViTs, focusing on their ability to handle complex and diverse datasets. A detailed comparative analysis is conducted, emphasizing performance metrics, robustness, and adaptability across different image recognition scenarios. The results reveal that while CNNs excel in traditional image processing tasks, ViTs demonstrate significant improvements in capturing long-range dependencies, thereby enhancing recognition accuracy in more complex contexts. This analysis offers critical perspectives on selecting and applying image recognition models, guiding future exploration and practical use in various industries. It underscores the impact of deep learning innovations on advancing image recognition capabilities and highlights potential directions for ongoing development in the field.

Research on gesture recognition technology based on machine learning

Abstract. Hand Gesture Recognition (HGR), as a significant technological advancement in the field of Human-Computer Interaction (HCI), aims to develop systems capable of accurately recognizing and interpreting human gestures for a diverse range of applications, including device control, virtual reality, gesture passwords, and gesture interaction. With the continuous advancement of machine learning algorithms, especially deep learning techniques, machine learning-based gesture recognition has garnered widespread attention. This paper presents a review of the development of gesture recognition techniques from traditional approaches to the current mainstream deep learning-based methods, and outlines the challenges and technical difficulties encountered. It analyzes several of the most popular classification techniques, including Naive Bayes, K-Nearest Neighbors (KNN), Random Forests, XGBoost, Support Vector Classifiers (SVCs), and Convolutional Neural Networks (CNNs). Furthermore, this paper examines the application of these algorithms in both dynamic and static gesture recognition and compares their performance and suitability in different scenarios. The results demonstrate that the accuracy and robustness of gesture recognition systems can be markedly enhanced through the prudent selection and optimization of the algorithms, which serves as a valuable reference for future research and applications.

Remote sensing image scene recognition using MobileNet

Abstract. Remote sensing image scene recognition plays a pivotal role in various applications, including environmental monitoring, disaster response, urban planning, precision agriculture, and aids in resource management and policy formulation. However, utilizing established convolutional neural networks(CNNs) models like AlexNet and VGG9 for this task can be computationally intensive and time-consuming due to their extensive parameter requirements. This dissertation introduces a MobileNet-based CNN optimized for remote sensing image scene recognition. This lightweight model significantly reduces computational load and model size without compromising accuracy, thereby enhancing efficiency. Empirical results on the NWPU45 dataset demonstrate MobileNet's superiority, achieving an accuracy of 91.16%, a Kappa coefficient of 90.96%, and an F1 score of 91.16% on the test set. Moreover, MobileNet's compact architecture, with merely 3.2531 million parameters and 587.9342 million FLOPs, underscores its efficiency and makes it a promising candidate for practical deployment in remote sensing applications. The findings suggest that MobileNet not only addresses the challenge of computational intensity but also opens new avenues for advancing scene recognition technology in the field of remote sensing.

Bias in the Algorithm: Issues Raised Due to Use of Facial Recognition in India

From helping you and me unlock our smartphones with a mere glance to enabling the police to identify criminals via CCTV footage, artificial intelligence (AI)-powered face recognition technology (FRT) is rapidly advancing by the day. According to reports, the Indian Railways will install FRT-based video surveillance systems in 983 railway stations across the country to ramp up security. However, the accuracy and reliability of FRT depend on the quality of the input images, the algorithms used and the size and quality of the reference database. Due to the scope for major imbalances in these elements, these algorithms have been found to be biased—largely against minority communities and women, thereby exacerbating already prevalent forms of societal discrimination. Scrutinising key research studies that collectively expose racial and gender bias prevalent in widely adopted FRT tools in the United States as well as India, this article analyses how bias and inaccuracy of these tools have led to poor outcomes and raised concerns when deployed by law enforcement agencies in India. Furthermore, this article traces the historical context of these biases and proposes debiasing measures that go beyond balancing datasets.

Research on CAPTCHA recognition technology based on deep learning

Abstract. CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), a security technique widely used on the Internet, can be utilized to distinguish human users from automated programs. The rapid development of deep learning technology has led to the emergence of graph-based recognition techniques that demonstrate excellent performance, which has prompted the investigation of CAPTCHA recognition based on deep learning as a research area of significant interest. This paper addresses the challenging problem of CAPTCHA recognition based on deep learning techniques, reviews the development and classification of CAPTCHA, examines traditional CAPTCHA recognition methods, and delves into the application of deep learning in CAPTCHA recognition. Therefore, a CAPTCHA recognition system is designed and its effectiveness is verified through experiments. This paper makes a significant contribution to the field of CAPTCHA recognition by proposing a deep learning-based approach, which not only enhances the accuracy and efficiency of CAPTCHA recognition, but also provides new ideas and methods for the further development. In the future, further research will be conducted in the field of CAPTCHA recognition to explore additional deep learning models and techniques with the aim of boosting the security and user experience of CAPTCHA.

The contribution of facial recognition technology to wrongful arrests and trauma.

When a crime is captured on video, law enforcement agencies increasingly have used facial recognition technology (FRT) to generate suspects to investigate. However, there are increasing examples of people who have been wrongfully arrested based because of the inaccurate results returned from these artificial intelligence-assisted searches of facial databases, despite very low error rates in the accuracy of these systems. We discuss the reliability of the evidence provided by a match returned by FRT, propose a framework for identifying potential problems with the use of FRT in criminal investigations, and review the research on the general trauma that comes from justice system involvement, trauma that is compounded by wrongful arrest and conviction. We also provide an analysis of how database size affects the evidentiary value of the matches returned by FRT. Variables like facial database size, race of the culprit, and quality of the probe photo can increase the likelihood that FRT systems will return false positive matches. The use of FRT for developing suspects in criminal investigations is likely to exacerbate the already profound racial disparities in the outcomes produced by the criminal legal system and increase trauma experienced by those who are wrongfully arrested or convicted. We recommend extreme caution surrounding its use. In addition, we call for more research on the trauma associated with wrongful arrest, which is likely to occur with the current use of FRT. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Human motion and musical expression: Exploring the link between physical movement and emotional resonance in performance

Musical performance has traditionally been understood as an auditory experience, with emotions conveyed primarily through sound. However, emerging research highlights the role of physical movement—gestures, posture, and body language—in enhancing emotional expression and creating a deeper connection between performers and their audiences. While existing studies have explored some aspects of this relationship, there is limited understanding of how specific movements modulate emotional responses across different musical genres. This study addresses these gaps by investigating the interaction between human motion and musical expression, focusing on the emotional resonance experienced by performers and audience members. The need for this study arises from the lack of comprehensive data on the correlation between movement and emotional engagement during musical performances, particularly concerning different genres. Using motion capture, biometric sensors, and facial recognition technology, the research analyzed the performances of professional and amateur musicians across classical, jazz, and contemporary music. Audience members’ emotional responses were captured through physiological data and post-performance surveys. One limitation of this study is that it focused solely on live performance environments, leaving digital or virtual performances unexplored. Key findings reveal that expressive movements increased emotional intensity ratings by 1.6 points (p < 0.001), with heart rate rising by 6.9 BPM (p < 0.0012) and electrodermal activity increasing by 1.6 µS (p < 0.0008). Movement synchronization with musical elements, especially during climaxes, strongly correlated with heightened emotional responses (r = 0.85, p < 0.01). Classical performances exhibited the highest synchronization between movement and emotion, with audience emotional intensity peaking at 8.3 on a Likert scale.