Face Detection Research Articles

Automatic face detection based on bidirectional recurrent neural network optimized by improved Ebola optimization search algorithm.

Face detection is a multidisciplinary research subject that employs fundamental computer algorithms, image processing, and patterning. Neural networks, on the other hand, have been widely developed to solve challenges in the domains of feature extraction, pattern detection, and the like in general. The presented study investigates the DNN (deep neural networks) use in the creation of facial detection operating systems. In this study, a novel optimized deep network has been presented to face detection. In this paper, after using some preprocessing stages for contrast enhancement and increasing the data number for the next deep tool, they fed to a bidirectional recurrent neural network (BRNN). The network is optimized via a novel enhanced version of Ebola optimization algorithm to provide far greater accuracy. The suggested procedure is examined on GTFD (Georgia Tech Face Database) and the results indicate that the proposed technique significantly outperforms other comparative methods, attaining an accuracy of 94.3%, a precision of 93.51%, a recall of 94.53%, and an F1-score of 92.47%. Furthermore, the method exhibits resilience against various challenges, achieving an accuracy of 95.6% under occlusions, 96.3% under lighting variations, 94.8% under pose variations, and 92.4% under low resolution conditions. Simulation results depict that the suggested technique gives far greater accuracy in comparison with the other comparative approaches.

Modeling Of Mask Detection Systems, Distance Between Objects And Facial Recognition Using The Tiny-YOLOV4 Method, Convolutional Neural Network, And Viola Jones

The development of technology is currently increasing very rapidly, the latest technology such as Face Detection and Face Recognition in public facilities has been widely applied, such as cameras for attendance, facial detection devices as mobile phone security and various other new innovations in the field of technology. The purpose of creating this model is to determine the distance, and the use of masks in public places and to determine the performance when the two algorithms between Tiny-YOLOV4 and Viola Jones are combined. This method uses CPU testing with a clock speed of 2.9 GHz and GPU with a clock speed of 1590 MHz with an accuracy level of 92.6% for mask object detection and 90.67% for face recognition with a maximum distance in front of the camera for human detection is 830 centimeters, mask detection 730 centimeters, and face recognition 530 centimeters. The results of the study produced an accuracy of 92.6% for mask detection and an accuracy of 90.67% for face recognition at maximum distances of 730 cm and 530 cm respectively. The conclusion shows that this system is effective for detecting masks, faces, and distance, providing a significant tool for monitoring compliance in public spaces. Implementing this system can serve as a preventive measure for potential future pandemics

A Review of Facial Expression Recognition Based on Convolutional Neural Networks

Abstract. The intelligent processing of human facial expressions has gained popularity due to the growth of big data and the expanding knowledge of AI and machine learning. In this paper, the author reviews the current mainstream methods for face recognition using convolutional neural network (CNN) models in deep learning, providing insights and future directions. By collecting and analyzing research findings, the characteristics, strengths and weaknesses of each model are discussed. The results indicate that CNN-based face recognition systems typically involve several steps: face detection, face alignment, face representation, face matching, and post-processing. By examining expression recognition with models like LeNet-5, AlexNet, VGGNet, and GoogleNet, the author concludes that CNNs possess features such as being data-driven, capable of feature learning, multi-level structured, adaptable to different domains, capable of real-time processing, and supporting multi-task learning. This technology has made significant strides and offers considerable potential for further development.

Implementation of Automatic Attendance System Based on Face Recognition Using CNN Method in IPB University Vocational School Environment

The research focuses on creating an automated attendance system using face recognition through the Convolutional Neural Network (CNN) approach at IPB University's Vocational School. The current manual attendance methods show limitations, such as potential inaccuracies in recording and the risk of cheating, like attendance proxies. To overcome these challenges, this study applies the CNN approach with Python and OpenCV, enabling automatic face detection and recognition for students. The system accurately logs attendance by identifying faces in real time. Testing indicates that the system records attendance reliably, whether with a single individual or with multiple faces present within a single frame.

FaceFinder: A Machine Learning Tool for Identification of Facial Images from Heterogenous Datasets

PurposeTo develop an algorithm to automate the organization of large photo databases using the Haar cascade algorithm for face and eye detection using machine learning tools in Python. DesignRetrospective study for the purposes of clinical tool development. MethodsWe developed an algorithm, termed FaceFinder, to identify front facing images in a large dataset of facial, orthodontal and miscellaneous images. FaceFinder works by detecting the presence of faces and at least two eyes using the Haar cascade. Execution time was recorded using different-sized datasets. A total of 895 images were analyzed by FaceFinder using various thresholds for face and eye detection. Precision, recall, specificity, accuracy, and F1 score were computed by comparison to ground truth labels of the images as determined by a human grader. ResultsUsing medium thresholds for face and eye detection, FaceFinder reached recall, accuracy, and F1 score of 89.3%, 91.6%, and 92.9%, respectively with an execution time per image was 0.91 seconds. Using the highest threshold for face and eye detection, FaceFinder achieved precision and specificity values of 98.3% and 99.2% respectively. ConclusionsFaceFinder is capable of sorting through a heterogenous dataset of photos of patients with craniofacial disease and identifying high-quality front-facing facial images. This capability allows for automated sorting of large databases that can facilitate and expedite data preparation for further downstream analyses involving artificial intelligence and facial landmarking.

A Fuzzy-Based Transformation to Mitigate the Impact of Adversarial Attacks in Deep Learning-Based Real-Time Surveillance Systems

Deep learning (DL) techniques have become integral to smart city projects, including video surveillance systems (VSS). These advanced technologies offer significant benefits, such as enhanced accuracy and efficiency in monitoring and managing urban environments. However, despite their advantages, these systems are not without vulnerabilities. One of the most pressing challenges is their susceptibility to adversarial attacks, which can lead to critical misclassifications during inference. To address these challenges, our research focuses on developing a more robust smart city VSS. Our research unfolds across two pivotal initiatives. In our initial exploration, we introduce a pioneering framework that extends the reach of adversarial attacks to real-time VSS. A practical manifestation involved implementing a real-time face mask surveillance system based on Multi-Task Cascaded Convolutional Networks (MTCNN) for face detection and MobileNet-v2 for face mask classification, subjecting it to the Fast Gradient Sign Method (FGSM) adversarial attack in real-time. In our subsequent endeavor, we propose a sophisticated defense mechanism deploying Fuzzy Image Transformation as a pre-processing unit (FITP). This strategic defense fortification significantly reinforces our real-time VSS against adversarial intrusions. Experimental findings highlight the effectiveness of the proposed adversarial attack framework in real-time, resulting in a marked reduction in the model's performance from a precision (P) of 93%, recall (R) of 93%, F1 score (F) of 93%, and accuracy (A) of 93% to 22%, 21%, 22%, and 22%, respectively. However, the post-implementation efficacy of our defense mechanism is striking, enhancing the model's average performance to a noteworthy improvement, with P, R, F, and A ascending to 91%, 90%, 91%, and 91%. This research illuminates the vulnerabilities intrinsic to VSS in the face of adversarial threats, underscoring the critical need for heightened awareness and the development of robust defense mechanisms before real-world deployment.

Home Door Security System with Face Recognition Using ESP 32 Cam

The development of science and technology, especially in the fields of Electronics and Informatics, is expected to enhance room or home security systems, particularly home door security systems. Many door security technologies have been developed to prevent and ensure the comfort of homeowners when they leave their homes. Some commonly used door security systems include CCTV cameras, fingerprint sensors, voice sensors, and RFID. However, with technological advancements, these security systems have shown vulnerabilities, such as frequent errors in fingerprint and voice sensors. Based on this, to create a more secure and innovative home door security system, the authors developed a security system with face detection and a remote warning notification system using Telegram. The researchers created a door security system using the ESP 32-CAM for face detection and as a controller for the Internet of Things (IoT) system. Additionally, a manual system using a keypad for password input was also created. The results of this study include the accuracy and speed of face recognition and the speed of sending security system warning information through the Telegram application. Keywords— Door Lock System, Face Recognition, ESP32 Camera, Microcontroller, Internet of Things.

Implementation of the YOLO Method for Detection of Human Emotions Based on Facial Mimics

Emotion detection through facial expression recognition plays an important role in everyday life, such as how to respond correctly to emotional expressions in social interactions, so that you can establish and build verbal or nonverbal communication with other people and so on. Facial expressions are facial changes in response to a person's emotional state, intentions, or social communication. Face detection is the first step that must be taken in facial analysis, including facial expression recognition. There are many methods that can be used to carry out the face detection process, such as the YOLO method. This YOLO method reframes object detection as a single regression problem, directly from image pixels to bounding box coordinates and class probabilities. By using the YOLO method, the process only needs to look once at the input image, to predict what objects are in the image and where those objects are. Based on the results of the tests carried out, the YOLO method can be used to detect human facial expressions with a success rate of 80%, with neutral, surprise and disgust facial expressions having a good level of accuracy and fear facial expressions having a poor accuracy level. The YOLO method is able to detect facial expressions of humans who wear accessories such as glasses.

Advancements in Multi-Modal Face Recognition: A State-of-the-Art Review of Security Biometrics

This survey is a state-of-the-art review of recent advancements in face recognition, particularly multi-modal face recognition and its associated applications in the domain of security biometrics and identity verification. In this regard, the paper sheds light on the significance of the Sejong Face database and several other leading databases as equally significant tools in studying some of the most challenging tasks in face recognition: occluded and diverse face identification, cross-modality evaluation, and infrared-visible face detection. It also insists on the high challenge of face identification across scenarios and with different forms of disguise. In addition, it emphasizes the need for an effective security framework to be implemented with other major sectors that deal in finances. The purpose is to develop an exhaustive list of methodologies and innovations based on the Sejong Face Database with the intention of finding new methods and algorithms that guarantee accuracy and reliability. Specifically, the application of deep learning methods comprising convolutional neural networks with cross-modality discriminator networks and unit-class loss demonstrates significant advancements in the development of face recognition systems; consequently, these systems exhibit enhanced security and efficacy. The review article is an important source that points out the current status of the research, as well as future possibilities, regarding biometric authentication and its implications in the digital scenario.

Attendance Marking using Face Detection

The demand for automated attendance management systems has significantly increased in educational institutions and corporate environments where manual methods are prone to inefficiencies and errors. This paper presents a real-time, facial recognition-based attendance system developed using Flask, OpenCV, and machine learning techniques. The system employs OpenCV’s Haar Cascade classifier for face detection and the K-Nearest Neighbours (KNN) algorithm for face recognition. During user registration, the system captures 10 images per individual to ensure robust recognition across various facial expressions. The web interface, built using Flask, facilitates user interaction for managing attendance records stored in CSV format. The system achieves an accuracy of 92% under optimal lighting conditions, though performance decreases to 80% in low-light environments. Recognition is completed in approximately 2 seconds, making the system suitable for real-time applications in classrooms or offices. Despite handling real-time image streaming efficiently, challenges such as reduced accuracy when handling obstructions like masks or glasses remain. Future enhancements will explore the integration of deep learning models, such as Convolutional Neural Networks (CNNs), to improve robustness and scalability. This system demonstrates the potential of facial recognition technology for automating attendance tracking, with significant applications in both educational and corporate sectors

ADYOLOv5-Face: An Enhanced YOLO-Based Face Detector for Small Target Faces

Benefiting from advancements in generic object detectors, significant progress has been achieved in the field of face detection. Among these algorithms, the You Only Look Once (YOLO) series plays an important role due to its low training computation cost. However, we have observed that face detectors based on lightweight YOLO models struggle with accurately detecting small faces. This is because they preserve more semantic information for large faces while compromising the detailed information for small faces. To address this issue, this study makes two contributions to enhance detection performance, particularly for small faces: (1) modifying the neck part of the architecture by integrating a Gather-and-Distribute mechanism instead of the traditional Feature Pyramid Network to tackle the information fusion challenges inherent in YOLO-based models; and (2) incorporating an additional detection head specifically designed for detecting small faces. To evaluate the performance of the proposed face detector, we introduce a new dataset named XD-Face for the face detection task. In the experimental section, the proposed model is trained using the Wider Face dataset and evaluated on both Wider Face and XD-face datasets. Experimental results demonstrate that the proposed face detector outperforms other excellent face detectors across all datasets involving small faces and achieved improvements of 1.1%, 1.09%, and 1.35% in the AP50 metric on the WiderFace validation dataset compared to the baseline YOLOv5s-based face detector.

FERDCNN: an efficient method for facial expression recognition through deep convolutional neural networks

Facial expression recognition (FER) has caught the research community’s attention recently because it can affect many real-life applications. Multiple studies have focused on automatic FER, most of which use a machine learning methodology, FER has continued to be a difficult and exciting issue in computer vision. Deep learning has recently drawn increased attention as a solution to several practical issues, including facial expression recognition. This article introduces an efficient method for FER (FERDCNN) verified on five different pre-trained deep CNN (DCNN) models (AlexNet, GoogleNet, ResNet-18, ResNet-50, and ResNet-101). In the proposed method, firstly the input image has been pre-processed using face detection, resizing, gamma correction, and histogram equalization techniques. Secondly, the images go through DCNN to extract deep features. Finally, support vector machine (SVM) and transfer learning are used to classify generated features. Recent methods have been employed to evaluate and contrast the performance of the proposed approach on two publicly standard databases namely, CK+ and JAFFE on the seven classes of fundamental emotions, including anger, disgust, fear, happiness, sadness, and surprise beside neutrality for CK+ and contempt for JAFFE. The suggested method tested Four different traditional supervised classifiers with deep features, Experimental found that AlexNet excels as a feature extractor, while SVM demonstrates superiority as a classifier because of this combination achieving the highest accuracy rates of 99.0% and 95.16% for the CK+ database and the JAFFE datasets, respectively.

Research on the detection and recognition system of target vehicles based on fusion algorithm

<p>In modern society, the monitoring of vehicles is paramount for upholding traffic safety and order. This paper delves into a Graphical User Interface (GUI)-driven system for vehicle target detection and integration. This system seamlessly integrates three pivotal functionalities: vehicle type recognition, license plate recognition, and driver face recognition. It automates the vehicle inspection assessment process through an intuitive user interface. Specifically, for license plate recognition, the system leverages traditional computer vision techniques, including color and grayscale processing, radon transform, morphological operations, edge detection, character segmentation, and character recognition. The driver face recognition module incorporates methods such as image gray scaling, Gaussian filtering for denoising, face detection, and feature extraction. Meanwhile, vehicle type recognition is achieved by extracting Histogram of Oriented Gradients (HOG) features and utilizing a Support Vector Machine (SVM) classifier. Experimental findings highlight the system’s remarkable recognition accuracy, offering a highly efficient and dependable solution for the automated inspections of vehicles.</p>

Object Detection Algorithms for Video Surveillance Applications

Abstract—Object Detection algorithms find applications in various fields, such as defense, security, and healthcare. In this paper, various Object Detection Algorithms, such as face detection, skin detection, color detection, shape detection, and target detection, are simulated and implemented using MATLAB 2017b to detect various types of objects with improved accuracy for video surveillance applications. Further, various challenges and applications of object detection methods are elaborated.

GCS-YOLOv8: A Lightweight Face Extractor to Assist Deepfake Detection.

To address the issues of target feature blurring and increased false detections caused by high compression rates in deepfake videos, as well as the high computational resource requirements of existing face extractors, we propose a lightweight face extractor to assist deepfake detection, GCS-YOLOv8. Firstly, we employ the HGStem module for initial downsampling to address the issue of false detections of small non-face objects in deepfake videos, thereby improving detection accuracy. Secondly, we introduce the C2f-GDConv module to mitigate the low-FLOPs pitfall while reducing the model's parameters, thereby lightening the network. Additionally, we add a new P6 large target detection layer to expand the receptive field and capture multi-scale features, solving the problem of detecting large-scale faces in low-compression deepfake videos. We also design a cross-scale feature fusion module called CCFG (CNN-based Cross-Scale Feature Fusion with GDConv), which integrates features from different scales to enhance the model's adaptability to scale variations while reducing network parameters, addressing the high computational resource requirements of traditional face extractors. Furthermore, we improve the detection head by utilizing group normalization and shared convolution, simplifying the process of face detection while maintaining detection performance. The training dataset was also refined by removing low-accuracy and low-resolution labels, which reduced the false detection rate. Experimental results demonstrate that, compared to YOLOv8, this face extractor achieves the AP of 0.942, 0.927, and 0.812 on the WiderFace dataset's Easy, Medium, and Hard subsets, representing improvements of 1.1%, 1.3%, and 3.7% respectively. The model's parameters and FLOPs are only 1.68 MB and 3.5 G, reflecting reductions of 44.2% and 56.8%, making it more effective and lightweight in extracting faces from deepfake videos.

Face Detection: Its Role In Advancing Lip-Reading Technology

Face Detection: Its Role In Advancing Lip-Reading Technology

Contactless Service: Artificial Intelligence Applications of Airports

Airports are an essential part of the airline value chains. Artificial intelligence (AI) technologies are widely used at airports; they utilize two technologies: face detection and recognition. The study examines how passengers' performance expectancy and emotions affect satisfaction and willingness to adopt the AI service of passengers via familiarity. Based on the AI device, it uses acceptance and socio-technical systems perspectives; participants who adopted the AI service in airports in Beijing, Taipei, and Singapore who have the potential to use AI service usage experience more than three times; 387 completed questionnaires. A satisfaction model was analyzed using the partial least squares approach; this study used purposed sampling to collect data based on the passenger volume analysis by those airports, and 387 completed questionnaires were analyzed. With the rise of AI innovation services in airports, passengers can quickly complete boarding procedures through AI innovative services. The research findings point out that passengers' performance expectancy and emotions positively affect familiarity with AI services for passengers. AI innovations provide a service change driver for and have the potential to replace human tasks and intelligence and analytics applications at airports. AI services are a powerful tool for airports to serve their passengers efficiently, and airports will collaborate with airlines to serve passengers with AI.

Design of a Safe Security System Based on Internet of Things Using Face and Fingerprint Detection

Traditional safe security systems usually use manual keys and a combination of numbers or passwords to open the safe. This system has several disadvantages such as being easy to break into, cumbersome, and the owner easily loses the key, even forgets the password needed to open the safe, which causes the safe to be unable to open. This research develops an Internet of Things (IoT)-based safe security system that uses two security options to open the safe, namely face detection and fingerprint authentication to increase security against unauthorized access with a prototype method. The system uses the ESP32-CAM to capture facial images and send them to the Telegram app for manual verification by the owner, while the fingerprint sensor ensures only registered users can open the safe. Arduino Uno serves as the main microcontroller to manage the integration between components such as ESP32-CAM, fingerprint sensor, relay, solenoid lock, LCD, and buzzer. The test results show that this system is effective in providing security to the safe through notifications, although it still relies on manual verification of faces via Telegram and requires a stable internet connection, and fingerprints that have been registered are successfully implemented. Further development is recommended to automate face recognition and improve the overall performance of the system.

Real-time Performance Comparison of Face Detection Algorithms using Raspberry Pi

Abstract This study reviewed state-of-the-art face-detection techniques like Haar cascade, Dlib HOG, MTCNN, and MediaPipe; implemented and tested them on Raspberry Pi, and evaluated their accuracy, speed, and frames per second. Overall, the research underscores the practical challenges of face detection, including varying lighting, facial expressions, occlusions, poses, scale of face, and accessories, and provides valuable insights for developers and researchers working on edge AI applications on low-cost edge devices. The study found that the MediaPipe face detection algorithm demonstrated robust performance, even with low-quality images, and showed good efficiency and resource management on the Raspberry Pi. The research emphasized the importance of considering factors like accuracy, speed, and resource efficiency in face detection on edge devices. The findings suggest that MediaPipe is a strong candidate for applications requiring efficient face detection, affordable and versatile platforms like Raspberry Pi. By focusing on the Raspberry Pi, the study offers a unique perspective on the performance of state-of-the-art face detection algorithms in real-world, resource-constrained environments, making it a significant contribution to the field of face recognition and edge computing.

Design of a Guest Face Detection Tool Using ESP32-CAM Based on Internet of Things (IoT)

This research develops an Internet of Things (IoT)-based guest face detection tool using ESP32-CAM to create a digital guestbook archive. The device distinguishes between guests and employees, with employee face data stored in a GitHub repository. When the device detects an unfamiliar guest face, a notification and photo are sent to a Telegram bot. If an employee's face is detected, a notification of the employee's name is sent to the Telegram bot. The method used is prototyping and the test results show that this tool is effective in detecting and digitally archiving guest visits.