Face Detection Algorithm Research Articles

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.

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.

Application of Visual Transformer in Low-resolution Thermal Infrared Image Recognition

Abstract Addressing the challenges of inadequate accuracy and limited robustness exhibited by current lightweight object detection networks specifically tailored for low-resolution thermal infrared face detection scenarios, this paper delves into developing an ultra-lightweight thermal infrared face detection algorithm that leverages visual attention mechanisms. To ascertain the optimal neural network complexity, a series of comparative experiments are meticulously conducted. With Yolo-FastestDet serving as the benchmark, this study endeavors to compress the backbone network, striking a delicate balance between network depth and detection speed. Additionally, to bolster the network’s capacity for profound feature extraction and precise discrimination of target edges and small objects, a Transformer-Encoder-based visual attention module is seamlessly integrated. Consequently, a lightweight face detection algorithm, enriched with attention mechanisms, is formulated. Furthermore, to mitigate the scarcity of low-resolution infrared face image data, a self-constructed visible-infrared face dataset is employed for training and evaluation purposes. The experimental outcomes reveal that the proposed algorithm attains an impressive mAP@0.5 score of 0.953 on the test dataset while satisfying the stringent real-time detection criterion of 30 frames per second (FPS) when deployed on an embedded Raspberry Pi CPU.

Quarantine Order Violators System using Face Recognition (FACID)

Face recognition technology is commonly utilized for user authentication and verification by analysing a digital image of a person's face and matching it against a database of faces for identification purposes. The COVID-19 pandemic has led to mandatory quarantines and the use of quarantine bracelets for some individuals, which can be time-consuming and require a lot of effort. Face recognition technology can help raise awareness about the current situation and alleviate some of the burden associated with quarantine measures. The aim of this research is to concentrate on the face recognition of individuals with a history of quarantine, as a measure to prevent the spread of COVID-19 in educational institutions like universities, colleges, and schools. This research study concentrates on individuals with a history of quarantine orders. The system will employ the Histograms of Oriented Gradients (HOG) algorithm for face detection. Additionally, the system will utilize the Face Landmark Algorithm to compare the 128-d vector with images stored locally. The system will make use of the Helen face collection dataset for its data requirements. The aim of this study is to explore and identify techniques for detecting individuals who violate quarantine measures and issuing notifications through the proposed system. By implementing this system, it could contribute to creating a more secure environment within the educational institution and potentially reduce the spread of the virus.

DEVELOPMENT OF AN ELECTRONIC EXAMINATION PLATFORM USING FACE RECOGNITION METHODS

Online systems face a major challenge in efficiently monitoring participants and students throughout lectures, particularly during exams. Establishing robust approaches and technologies is essential for identifying unjust, unethical, and illegal conduct in educational environments, particularly during academic courses and examinations. This study introduces a novel online proctoring system that employs deep learning to continuously supervise physical locations without requiring a human proctor. The system utilizes face detection and recognition algorithms to implement biometric procedures, including facial recognition. This work presents a novel approach for face recognition training, which involves an incremental training procedure. This eliminates the requirement for an additional step, resulting in reduced computation cost and time. In order to achieve high accuracy, the suggested model evaluated three distinct face detectors: HOG, MTCNN, and Yoloface. The assessment of the suggested model demonstrates that the HOG approach has surpassed the others. The proposed approach yields a significantly high accuracy rate.

A Face Detection System: A Comprehensive Survey

The domain of facial detection has witnessed significant advancement, mostly attributed to the progress in machine learning and deep learning methodologies. These enhancements have resulted in a fundamental change in the field. Face detection algorithms are extensively utilized in several domains, such as security, surveillance, and social networking applications. This survey has thoroughly investigated various methodologies for facial identification, with a focus on the challenges, applications, and evolution from traditional to deep learning-based approaches. Furthermore, it provides important insights into commonly used datasets for facial detection, including a comprehensive examination of their unique characteristics. The study is structured sequentially, commencing with an introduction to the assessment of significant previous research and the challenges faced by face detection algorithms. Subsequently, the investigation delves into different practical uses, delineates methods for identifying, and scrutinizes frequently employed data sets. The transition from traditional classifiers to deep learning models in face identification technology represents a significant improvement in addressing the complexities of face detection in various environments. Despite the obstacles that now exist, the continuous advancements in deep learning offer promising solutions that enhance the accuracy and efficiency of face identification systems in a wide range of applications.

Student Face Recognition and Alert Rule

Abstract: Attendance marking in a classroom during a lecture is not only an onerous task but also a time-consuming one at. Due to an unusually high number of students present during the lecture, there will always be a probability of proxy attendance(s). Attendance marking with conventional methods has been an area of challenge. The growing need for efficient and automatic techniques for marking attendance is a growing challenge in the area of face recognition. In recent years, the problem of automatic attendance marking has been widely addressed through the use of standard biometrics like fingerprint and Radiofrequency Identification tags, etc., However, these techniques lack the element of reliability. In this proposed project an automated attendance marking and management system is proposed by making use of face detection and recognition algorithms. Instead of using conventional methods, this proposed system aims to develop an automated system that records the student’s attendance by using facial recognition technology. The main objective of this work is to make the attendance marking and management system efficient, time-saving, simple, and easy. Here faces will be recognized using face recognition algorithms. The processed image will then be compared against the existing stored record and then attendance is marked in the database accordingly. Compared to the existing system traditional attendance marking system, this system reduces the workload of people. This proposed system will be implemented with 4 phases Image Capturing, Segmentation of group image and Face Detection, Face comparison and Recognition, and Updating of Attendance in the database. Index Term – Mongo DB, React JS, Node JS, Express JS, Attendance, Facial recognition, and detection, Haar cascade, Alert, Student Face.

YOLO-FaceV2: A scale and occlusion aware face detector

In recent years, face detection algorithms based on deep learning have made great progress. Nevertheless, the effective utilization of face detectors for small and occlusion faces remains challenging, primarily stemming from the limitations in pixel information and the presence of missing features. In this paper, we propose a novel real-time face detector, YOLO-FaceV2, built upon the YOLOv5 architecture. Our approach introduces a Receptive Field Enhancement (RFE) module designed to extract multi-scale pixel information and augment the receptive field for accurately detecting small faces. To address issues related to face occlusion, we introduce an attention mechanism termed the Separated and Enhancement Attention Module (SEAM), which effectively focuses on the regions affected by occlusion. Furthermore, we propose a Slide Weight Function (SWF) to mitigate the imbalance between easy and hard samples. The experiments demonstrate that our YOLO-FaceV2 achieves performance exceeding the state-of-the-art on the WiderFace validation dataset. Source code and pre-trained model are available at https://github.com/Krasjet-Yu/YOLO-FaceV2.

Face detection method based on improved YOLO-v4 network and attention mechanism

Abstract Due to insufficient information and feature extraction in existing face-detection methods, as well as limited computing power, designing high-precision and efficient face-detection algorithms is an open challenge. Based on this, we propose an improved face detection algorithm. First, through 1 × 1’s common convolution block (CBL) expands the channel for feature extraction, introduces a depthwise separable residual network into the YOLO-v4 network to further reduce the amount of model computation, and uses CBL to reduce the dimension, so as to improve the efficiency of the subsequent network. Second, the improved attention mechanism is used to splice the high-level features, and the high-level features and the shallow features are fused to obtain the feature vectors containing more information, so as to improve the richness and representativeness of the feature vectors. Finally, the experimental results show that compared with other comparative methods, our method achieves the best results on public face datasets, and our performance in personal face detection is significantly better than other methods.

Enhancing Driver Safety: Deep Learning Approach for Drowsiness Detection and Accident Prevention

Road safety concerns have spurred the development of innovative technologies aimed at reducing accidents, particularly those caused by driver fatigue. The main scope of the driver drowsiness detection system is to minimize road accidents caused by fatigue or sleepiness of drivers. This system leverages deep learning and computer vision, employing a Raspberry Pi camera to monitor facial expressions, including yawning, to assess the driver's alertness. Upon detecting signs of drowsiness, such as prolonged eye closure or altered facial expressions, the system triggers a buzzer alert by analyzing the Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) ratios. Integrated within an embedded system, it utilizes frontal face detection algorithms and Haar cascade classifiers to localize key facial features in real-time, facilitating efficient monitoring for signs of fatigue or distraction. Additionally, it issues vibration alerts if the seat belt is not fastened. The seat belt remainder will be helpful to ensure the safety of the driver, reduces accidents and monitors the driver's heart rate, triggering a PANIC message if irregularities are detected. At traffic signals, the system automatically reduces vehicle speed using a vibration motor. In the unfortunate event of an accident, the system initiates speed reduction and promptly notifies registered contacts through the Blynk application, thereby significantly reducing accidents caused by drowsy driving.

THE REAL TIME HIGH PRECISION CRIMINAL FACE RECOGNITION FOR ATM SECURITY IN DEEP LEARNING

The proposed ATM security system, utilizing composable deep face recognition, emphasizes user protection during transactions with a specific focus on eye retina accuracy. In the event of a potential threat identified through real-time face detection and tracking, an automatic alert is initiated. Prioritizing precision, the system employs advanced face detection algorithms for accurate location and tracking, even in dynamic scenarios. A down sampling technique ensures efficient processing of facial data, crucial for meticulous identification in crowded ATM locations. The integration of a face tracking ID unit enhances accuracy, providing continuous monitoring and persistence in identification, particularly focusing on the distinctive patterns in the eye retina. The scoring method, rooted in face tracking and embedding distance, significantly elevates user identification reliability, minimizing the risk of false positives, especially in criminal identification based on unique eye retina features. In response to potential threats, the system promptly generates automatic alerts and dispatches secure emails to relevant entities, including the affected card user, the respective bank, and cybersecurity authorities. These emails include comprehensive information, such as a high-precision image of the detected criminal's face, detailed facial recognition data, and a thorough summary of their known criminal history. This accuracy-driven and retina focused approach enhances safety, emphasizing continuous refinement and adherence to privacy and ethical considerations during implementation. KEYWORDS: Crime prevention, down-sampling, face recognition.

Facial Recognition Attendance System Using Python

Attendance marking in a school room all through a lecture isn't simplest a difficult project but additionally a time eating one at that. because of an surprisingly high variety of college students present all through the lecture there'll always be a probability of proxy attendance(s). Attendance marking with conventional methods has been an area of project. The developing need of efficient and automatic techniques of marking attendance is a growing venture within the place of face recognition. In current years, the hassle of automatic attendance marking has been broadly addressed through the usage of standard biometrics like fingerprint and Radio frequency identity tags and so on., however, these strategies lack the detail of reliability. in this proposed mission an automated attendance marking and management machine is proposed by making use of face detection and popularity algorithms. instead of the use of the conventional methods, this proposed system pursuits to increase an automatic machine that statistics the pupil's attendance by way of the use of facial popularity technology. the principle goal of this paintings is to make the attendance marking and management machine green, time saving, easy and smooth. right here faces might be recognized the usage of face popularity algorithms. The processed photo will then be compared towards the present stored document after which attendance is marked inside the database therefore. compared to present system traditional attendance marking device, this machine reduces the workload of people. This proposed gadget can be applied with 4 levels inclusive of image taking pictures, Segmentation of institution image and Face Detection, Face evaluation and popularity, Updating of Attendance in database

4AC-YOLOv5: an improved algorithm for small target face detection

In real scenes, small target faces often encounter various conditions, such as intricate background, occlusion and scale change, which leads to the problem of omission or misdetection of face detection results. To solve this puzzle, an improved algorithm of small target face detection 4AC-YOLOv5 is proposed. First, the algorithm by introducing a new layer to detect faces at a much smaller size, through the fusion of more shallow information, enhance the network perception of small objects, the accuracy of small target detection is improved; second, to improve the neck structure, to add the adaptive feature fusion network AFPN to replace FPN + PAN, to prevent the large information gap between non-adjacent Level to some extent, and to fully retain and integrate different scale characteristic information; and finally, improve the C3 module and propose a new multiscale residual module C3_MultiRes. Improving the expressive power of the network by introducing a multibranched structure and gradually increasing resolution somewhat reduces the complexity of the model calculation. The experimental results show that the precision of the improved model reached 94.54%, 93.08% and 84.98% in easy, medium and hard levels of WiderFace data set, respectively, and the results of detection are better than the original network. 4AC-YOLOv5 can meet the requirements of small target face detection in complex environment.

Improved DSFD Algorithm for Balancing Speed and Accuracy

Aiming at the mismatch between speed and accuracy of DSFD face detection algorithm, a lightweight network method based on improved DSFD is proposed. First, the backbone of the algorithm is replaced with EfficientNet B1, which greatly reduces parameters and computation. Then the feature pyramid network in feature enhance module is changed to BiFPN to further enhance the feature fusion. Finally, to improve the efficiency, dilated convolution layers in feature enhancement module are removed, and the convolution layers of BiFPN is replaced by ACBlock. The improved algorithm was evaluated on WIDER FACE and the effectiveness of the proposed methods are evaluated through ablation experiments. The detection speed of the improved algorithm is increased by twice. Compared with two popular algorithms, the improved algorithm achieves higher accuracy.

Fatigue Driving Detection Based on Driver Facial Temporal Sequences

Addressing the issues of low real-time performance and high false positive rates in driver fatigue detection methods based on deep learning, this paper proposes a temporal sequence Transformer-based fatigue detection method grounded in the localization of facial landmarks in drivers. Initially, the facial positions are obtained using the single-stage face detection algorithm RetinaFace. Subsequently, a lightweight GM module is designed as the principal feature extraction module for constructing a multi-scale fusion facial landmark detection network, and facial fatigue feature parameters based on temporal sequences are calculated according to the facial landmarks. Finally, a fatigue driving classification method based on temporal sequences Transformers is developed for classifying the sequences of fatigue feature parameters. Experimental results demonstrate that the inference time required for facial detection and landmark detection is merely 16.8 milliseconds, with the per-frame inference time for facial landmark detection being only 2.5 milliseconds, thus fulfilling the real-time requirements of fatigue driving detection during the feature extraction phase. A temporal sequence of fatigue feature parameters built on the NTHU-DDD dataset and the trained temporal sequence Transformer model resulted in an accuracy rate of 91.4% for the proposed method.

AI based Face Recognition Attendance System

The AI Based Face Recognition Attendance System represents an innovative solution at the intersection of artificial intelligence and attendance management. Leveraging Python open-source libraries such as OpenCV and NumPy, alongside machine learning techniques, the system aims to streamline the attendance tracking process. By employing face detection algorithms and feature extraction methods, the system identifies individuals, records their attendance, and updates the database in real-time. The system architecture incorporates components like a frontend web application, real-time prediction module, registration form module, reporting module, Redis database, Streamlit framework, Insightface library, and AWS deployment. Through continuous improvement and innovation, the system enhances user experience, accuracy, and efficiency in attendance management.

Research on improved small scale face detection algorithm based on Yolov5

To address the challenges of dense scenarios with densely distributed small-scale faces, severe occlusions, and unclear features leading to inaccurate detection and high miss rates, we propose a lightweight small-scale face detection algorithm based on YOLOv5. The aim is to enhance the accuracy and precision of target detection. Firstly, we introduce the Convolutional Block Attention Module (CBAM) into the existing backbone network, obtaining more detailed features by comprehensively considering both spatial and channel dimensions. Next, in the Neck network, we embed involution to enhance channel information and weight distribution. Finally, a new feature fusion layer is added to improve the capture capability of feature information for smaller pixels and smaller targets in visible areas by integrating deep semantic information with shallow semantic information. The experimental results demonstrate that the improved model exhibits an increase in the average precision across all three subsets of the public WIDER FACE dataset, with improvements of 3.2%, 3.4%, and 2.6% respectively. The detection frame rate reaches 87 frames per second (FPS), significantly enhancing the detection performance of facial targets. This improvement meets the accuracy and real-time requirements for detecting small-scale facial targets in dense scenarios.

Revisiting face detection: Supercharging Viola-Jones with particle swarm optimization for enhanced performance

In recent years, face detection has emerged as a prominent research field within Computer Vision (CV) and Deep Learning. Detecting faces in images and video sequences remains a challenging task due to various factors such as pose variation, varying illumination, occlusion, and scale differences. Despite the development of numerous face detection algorithms in deep learning, the Viola-Jones algorithm, with its simple yet effective approach, continues to be widely used in real-time camera applications. The conventional Viola-Jones algorithm employs AdaBoost for classifying faces in images and videos. The challenge lies in working with cluttered real-time facial images. AdaBoost needs to search through all possible thresholds for all samples to find the minimum training error when receiving features from Haar-like detectors. Therefore, this exhaustive search consumes significant time to discover the best threshold values and optimize feature selection to build an efficient classifier for face detection. In this paper, we propose enhancing the conventional Viola-Jones algorithm by incorporating Particle Swarm Optimization (PSO) to improve its predictive accuracy, particularly in complex face images. We leverage PSO in two key areas within the Viola-Jones framework. Firstly, PSO is employed to dynamically select optimal threshold values for feature selection, thereby improving computational efficiency. Secondly, we adapt the feature selection process using AdaBoost within the Viola-Jones algorithm, integrating PSO to identify the most discriminative features for constructing a robust classifier. Our approach significantly reduces the feature selection process time and search complexity compared to the traditional algorithm, particularly in challenging environments. We evaluated our proposed method on a comprehensive face detection benchmark dataset, achieving impressive results, including an average true positive rate of 98.73% and a 2.1% higher average prediction accuracy when compared against both the conventional Viola-Jones approach and contemporary state-of-the-art methods.

LighterFace Model for Community Face Detection and Recognition

This research proposes a face detection algorithm named LighterFace, which is aimed at enhancing detection speed to meet the demands of real-time community applications. Two pre-trained convolutional neural networks are combined, namely Cross Stage Partial Network (CSPNet), and ShuffleNetv2. Connecting the optimized network with Global Attention Mechanism (GAMAttention) extends the model to compensate for the accuracy loss caused by optimizing the network structure. Additionally, the learning rate of the detection model is dynamically updated using the cosine annealing method, which enhances the convergence speed of the model during training. This paper analyzes the training of the LighterFace model on the WiderFace dataset and a custom community dataset, aiming to classify faces in real-life community settings. Compared to the mainstream YOLOv5 model, LighterFace demonstrates a significant reduction in computational demands by 85.4% while achieving a 66.3% increase in detection speed and attaining a 90.6% accuracy in face detection. It is worth noting that LighterFace generates high-quality cropped face images, providing valuable inputs for subsequent face recognition models such as DeepID. Additionally, the LighterFace model is specifically designed to run on edge devices with lower computational capabilities. Its real-time performance on a Raspberry Pi 3B+ validates the results.

Face Detection Framework for Accelerated Analysis of High-Quality Multimedia Content

Modern face detection algorithms fail to provide optimal results when they have to deal with larger amounts of data per frame while processing higher quality videos. This paper tackles that problem and offers a solution to deploy commercially used state-of-the-art face detection algorithms to process only the regions of interest in a frame, and discard the rest to decrease the data to be processed. The model maintains the accuracy of the base algorithm while decreasing the processing time per frame, thereby increasing the overall efficiency. The selection of region of interest is dependent on the detection of facial window in the previous frame. Therefore, the choice of base algorithm plays an important role in determining the speed of the framework. The model achieves increased processing speeds of about 69–76% more than the standalone usage of the detection algorithms for analyzed frame rates.