Face Recognition Research Articles

A discriminative model for scale, translation and rotation invariant face recognition.

A discriminative model for scale, translation and rotation invariant face recognition.

Integrating NAO V6 robot for enhanced face recognition in security system

Integrating NAO V6 robot for enhanced face recognition in security system

Innovative approach of masked facial recognition

Innovative approach of masked facial recognition

Reply to Comment on Letter to the Editor: Humanitarian Facial Recognition for Rare Craniofacial Malformations.

Reply to Comment on Letter to the Editor: Humanitarian Facial Recognition for Rare Craniofacial Malformations.

Innovative approach of masked facial recognition

Innovative approach of masked facial recognition

Attention-Oriented Two-Stream Convolutional Networks for the Identification of Face Spoofing

Face recognition has been extensively researched and has seen tremendous success in a variety of applications over the past few decades because the human face retains the most information for identifying individuals. Modern face recognition systems are still vulnerable to face spoofing attacks, such as the face video replay attack. Despite the fact that numerous efficient antispoofing techniques have been put forth, we discover that illuminations impair the effectiveness of many of the current techniques. It encourages us to create illumination-invariant anti-spoofing techniques. In this work, we propose a two stream convolutional neural network (TSCNN) that operates on two complementary spaces: multi-scale retinex (MSR) space (illumination invariant space) and RGB space (original imaging space). In particular, MSR is invariant to illumination but contains less detailed facial information than RGB space, which contains detailed facial textures but is sensitive to illumination. Furthermore, the high-frequency information that is discriminative for face spoofing detection can be efficiently captured by MSR images. To learn the discriminative features for anti-spoofing, the TSCNN is fed images from two spaces. We suggest an attention-based fusion technique that can successfully capture the complementarity of two features in order to smoothly fuse the features from two sources (RGB and MSR). We test the suggested framework on a number of databases, including OULU, CASIA-FASD, and REPLAY-ATTACK, and we obtain very competitive results. We perform cross-database experiments to further confirm the suggested strategies' capacity for generalization, and the outcomes demonstrate the high efficacy of our approach.

초등 실과에서 효율적인 소프트웨어와 로봇 및 인공지능 교육을 위한 플랫폼의 개발

This study aimed to develop an educational platform to efficiently implement software, robotics, and artificial intelligence education in the revised 2022 elementary practical arts education curriculum. The results of the study are as follows. The revised 2015 curriculum posed various methodological difficulties that hindered efficient software and robotics instruction. First, there were difficulties in pairing block-based programming tools (software) with robots, the hardware that executes coding content. Second, teachers found monitoring each student's coding progress in real-time difficult, and answering individual questions required additional time, making personalized guidance difficult. Third, it took considerable time to share correctly coded programs with students for comparison and modification or to explain the correct coding methods. To address these challenges, this study developed a platform to efficiently facilitate software, robot, and coding education for artificial intelligence. The platform allows for easy pairing between programming tools and hardware and allows teachers and students to share coding content in real time. In addition, students can copy correctly coded programs to their own platforms in real time and modify or execute them. This platform is compatible with not only computers but also tablets and mobile phones, with capabilities such as voice recognition, image recognition, and machine learning. Image recognition functionality includes blocks for face, hand, and character recognition.

An Evaluation of MTCNN in Face Recognition Algorithms for Effective Detection in Masked Scenarios in Real Time Video Surveillance

An Evaluation of MTCNN in Face Recognition Algorithms for Effective Detection in Masked Scenarios in Real Time Video Surveillance

Research on a Lightweight Deep Learning Model Suitable for Face Recognition for Mobile Devices

Research on a Lightweight Deep Learning Model Suitable for Face Recognition for Mobile Devices

Efektivitas Robot AI Adaptif dalam Mendukung Pembelajaran Inklusif bagi Mahasiswa dengan Learning Disabilities di Universitas Darwan Ali

This study developed an adaptive AI robot as a learning aid for students with learning disabilities (LD) at Universitas Darwan Ali. The robot is equipped with text-to-speech, speech-to-text, voice and facial recognition features, and adaptive algorithms that adjust content delivery to match students' literacy levels. A research and development approach with agile methodology was employed, encompassing needs identification, design, testing, and implementation phases. Results indicate a 30% increase in academic comprehension and an 82% satisfaction rate among students. Additionally, the AI robot serves as a "learning companion" providing social-emotional support, which enhances learning motivation for LD students. Thus, this adaptive AI robot effectively supports inclusive education at Universitas Darwan Ali.

Rancang Bangun Sistem Keamanan Laboratorium Menggunakan Face Recognition Berbasis Internet of Thing (IoT)

An Internet of Things (IoT)-based laboratory security system is designed to improve security and monitoring efficiency. This study aims to develop such a system using ESP-32 CAM (Embedded Serial Peripheral Interface 32-bit Camera), a camera module with an ESP32 microcontroller with Wi-Fi connectivity and image processing capabilities for real-time access control and room monitoring. Firebase is a cloud-based facial data storage platform, and the Telegram application is a medium that allows automatic notifications. Access control testing was conducted involving five user samples. The system recorded a facial recognition success rate of 90% (9 out of 10 tests were successful), which is considered "good" based on the accuracy threshold of ≥ 85% for security applications. However, accuracy decreases in nonideal conditions, such as when the user's distance from the camera exceeds 30 cm or the angle of view of the face is not aligned with the camera. Non-ideal refers to operational situations where optimal parameters, such as distance and facial orientation, are unsatisfied. The notification delivery delay to the Telegram application was tested using Wireshark, with an average time of 18,528 ms, indicating that the system has a fast response in real-time. In addition, the monitoring camera (ESP-32 CAM) successfully sent stable real-time video to the laboratory manager's web without significant interference. The test results show that this system meets the design objectives as an efficient laboratory security solution. Some improvements are needed to improve accuracy under non-ideal conditions and expand monitoring testing to various network scenarios.

Raspberry Pi-based Facial Recognition for Vehicle Access Control

The integration of advanced biometric security systems in modern vehicles has become crucial to enhance safety and user convenience. This paper presents the design and implementation of a Raspberry Pi-based facial recognition system for vehicle access control, utilizing the power of computer vision and machine learning to provide secure, keyless vehicle entry. The system employs a Raspberry Pi microcontroller, coupled with a camera module, to capture and analyze the driver's facial features. OpenCV, a widely-used computer vision library, is integrated with a pre-trained deep learning model to perform facial detection and recognition in real-time. The system compares the captured facial data with pre-registered user profiles stored in a secure database. Upon a successful match, the Raspberry Pi sends a signal to unlock the vehicle, ensuring seamless and secure access. The necessity of adopting facial recognition technology for vehicle access lies in its non-invasive, hands-free nature, providing a more convenient and secure alternative to traditional key-based systems. Moreover, it adds an additional layer of security, minimizing the risk of unauthorized access through stolen keys or fob duplication. Experimental results demonstrate that the facial recognition system achieves a high accuracy rate of 95% under various lighting conditions and user angles. The average processing time for facial recognition is less than 1 second, ensuring swift vehicle access. Additionally, the system exhibits robust performance even with multiple registered users, confirming its scalability and practicality for real-world applications. This research highlights the potential of Raspberry Pi as a cost-effective, efficient platform for implementing facial recognition systems in vehicles, paving the way for further innovations in automotive security. Key Words: Raspberry Pi, Webcam, Image processing, the Main module

A TWO-STEP AUTHENTICATION FACIAL RECOGNITION SYSTEM FOR AUTOMATED ATTENDANCE TRACKING

This study addresses the need for efficient, automated attendance systems through the design of a facial recognition application. Manual attendance systems are slow, error-prone and the retrieval of old records can be tedious. Universally assessable technological developments such as facial recognition software can easily solve these problems. However, the vast amount of computational resources required for its implementation has posed a limitation to its wide adoption. This study presents a two-step approach to resolve these challenges. By leveraging a faster, less-powerful model, as the first step, the workload of facial recognition can be distributed to save time and computational cost. A more powerful machine learning model is applied as the second step, deployed for tasks that are too complex for the first model to handle. The two-step authentication process will also reduce the occurrences of false negatives. Face_recognition, a python library is used for detection and encoding of face images read using python’s opencv library from an IP webcam. A flask application demonstrates this facial recognition functionality. The database connection and communication are accomplished using flask_sqlalchemy. A graphical user interface (web application) is used to interact with users on a high level, showing saved images of logged personnel and their times of entry. The system has a maximum accuracy of 98.78% and precision of 98.82% from tests. This shows its potential for application on a wider scale, with some added improvements such as cloud deployment and larger datasets.

Facial expression recognition with modified local ternary pattern images using convolutional neural network and extreme learning machine

Facial emotion recognition is a crucial aspect of human-computer interaction systems, education, health and various other fields. The common challenges faced in FER are variation in light. Based on researches, Local ternary Pattern (LTP) as a feature vector that can handle the variation of light. Modified Local Ternary Pattern (MLTP) is more discriminative to variation of light and less sensitive to noise. MLTP is a conventional feature vector that requires manual processing. In contrast to MLTP, the Convolution Neural Network (CNN) architecture has an automated feature extractor. This paper proposes MLTP as input to the CNN architecture for handling the variation of light and for automatic feature extractor. Then, for classifying Extreme Learning Machine is used to reduce the training time of the CNN classifier. The Karolinska Directed Emotional Faces (KDEF) dataset is used to assess the proposed method and got an accuracy of 86.28%.

Kinship Verification Based on Intelligent Techniques: Survey

Kinship verification is the process of using observable characteristics, including facial features or other biometric data, to ascertain whether two people are familial related. Current approaches to kinship verification encounter multiple challenges, despite their significance in fields such as biometric security, family reunification, and forensic investigations. The most accurate way to confirm kinship is by DNA analysis, which is expensive, time-consuming, and invasive; in contrast, the traditional methods using manual comparison and facial recognition are more prone to human mistake. Visual verification of kinship among family members can be difficult to reliably rely on due to genetic variability, aging, and environmental influences. Automated methods that can reliably and efficiently confirm kinship using non-invasive methods are required, especially in situations where immediate identification is essential, like in the investigation of missing persons cases and disaster response. This study explores the use of different verification kinship techniques, ranging from various machine learning techniques reaching to up-to-date deep learning techniques. Finally, this study will discuss the datasets, preprocessing, methodology, advantages, disadvantages, and performance measures of selected state-of-the-art studies of kinship verification, and present the best technique used in kinship verification with the most related dataset. Index Terms— Kinship verification, Deep learning, Machine learning.

Effective Facial Expression Recognition System Using Artificial Intelligence Technique

Facial expressions are the most basic non-verbal method people use to communicate feelings, intentions and reactions without words. Recognizing these facial expressions accurately is essential for a variety of applications — such as tools that use our faces to interact with computers (human-computer interaction, or HCI), security systems and emotionally intelligent artificial intelligence technologies. As the complexities surrounding these relationships have become better understood, it has allowed us to develop increasingly more complex systems for identifying and detecting facial expressions of different emotions. This paper presents an improved performance of the Facial Expression Recognition (FER) systems via augmentation in Artificial Neural Networks and Genetic Algorithms, two renowned artificial intelligence techniques possessing disparate strengths. ANNS are inspired by the neural architecture of human brain capable of learning and recognizing patterns in unchartered data after trained examples, on the other hand GAs come from fundamental principles underlying natural selection perform optimization process based-on evolutionary methods which includes fitness evaluation, comparison, selection, crossover, and mutation. The research is an effort to mitigate the problems pertaining with conventional methods, like overfitting and generalization fault in order design FER model which has potential for performing much more accurately. A hybrid ANN-GA model that uses Petri Nets and production systems is proposed for the real-time video sequence analysis with high precision in predicting different dynamic facial activities of anger, surprise, disgust, joy, sadness and fear from emotion faces. Importantly, results on the study show that this integrated model has a large-scale promoting effect in emotion detection upon varied scenes and is therefore generalizable to many domains from security and surveillance over biomedicine up to interactive AI-driven systems. Implications for implementing real-time and context-aware recognition of human emotions based on AI technologies are far-reaching as they demonstrate the potential that hybrid AI systems offer at enhancing emotion deciphering.

A novel deep learning approach for facial emotion recognition: application to detecting emotional responses in elderly individuals with Alzheimer’s disease

A novel deep learning approach for facial emotion recognition: application to detecting emotional responses in elderly individuals with Alzheimer’s disease

Boosting Time Efficiency in Helmeted Face Recognition with Transfer Learning and k-NN

This research aims to optimize the retraining process of face recognition systems for motor vehicle parking areas using Transfer Learning and k-nearest Neighbors (k-NN). The system enhances efficiency by retraining only on new classes, identified by checking the encoding file, allowing it to skip pre-existing classes, thus significantly improving retraining speed. Face data from 100 individuals (2725 images) was collected and augmented, with the VGG16 architecture used to generate face embeddings through Transfer Learning. Achieving 95.62% accuracy, with a precision, recall, and F1-score of 0.96 at k=1, the system demonstrates high accuracy and speed in identifying registered faces while effectively rejecting unknown ones. These optimizations greatly increase the feasibility of face recognition in parking security systems, making them more responsive to new data. Future research should focus on increasing dataset diversity and exploring more complex conditions, such as varied lighting, to enhance system performance and robustness further.

FROM ORIGINS TO INNOVATIONS: AI'S ROLE AND THE COST IMPACT ON COMPUTER VISION

The field of Computer Vision, a pivotal subdomain of Artificial Intelligence (AI), has seen extraordinary advancements since its emergence in the 1960s. This paper examines the historical development of Computer Vision technologies, tracing the journey from early foundational models, such as Frank Rosenblatt’s Perceptron, to contemporary breakthroughs driven by Deep Learning. Key milestones are explored, including the development of algorithms like Scale-Invariant Feature Transform (SIFT), Viola-Jones for face detection, and Eigenfaces, which paved the way for modern solutions such as Convolutional Neural Networks (CNNs), YOLO and FaceNet. The paper highlights the evolution of face detection and recognition techniques, contrasting traditional methods with the transformative capabilities of Deep Learning-driven approaches. Additionally, we analyze the growing computational demands of modern algorithms, discussing the trade-offs between accuracy and efficiency and their implications for practical applications. This study underscores the rapid progression of Computer Vision, its challenges, and its role as a cornerstone in shaping the future of Artificial Intelligence.

The Role of Artificial Intelligence in Aviation Construction Projects in the United Arab Emirates: Insights from Construction Professionals

The applications of Artificial Intelligence (AI) in the airport industry are significantly transforming operational efficiency, safety, and passenger experiences. This study investigates the integration of AI within aviation construction projects, with a focus on the United Arab Emirates (UAE). While AI technologies such as facial recognition, IoT, and biometric systems have advanced airport security and operations, their use in construction project management remains limited. A survey was conducted among 101 engineering professionals and experts with experience or involvement in managing aviation-related construction projects. Participants, many of whom had familiarity with AI tools, provided insights into the applicability of AI in areas such as planning, scheduling, and safety monitoring. The majority agreed that AI has the potential to revolutionize project management processes, improving decision-making, and efficiency. AI tools can predict delays, optimize workflows, and enhance safety through real-time data analytics and machine learning algorithms, reducing risks and human error. Despite the UAE’s leadership in AI-driven security advancements, its use in aviation construction is still underdeveloped. This research highlights the potential for broader AI integration across the entire lifecycle of aviation projects. By adopting AI in these areas, UAE airports could set new benchmarks for cost effectiveness, sustainability, and project delivery, reinforcing the region’s status as a leader in technological innovation within the aviation industry.