Search Results (19)

Face recognition technology utilizes unique facial features to analyze and compare individuals for identification and verification purposes. This technology is crucial for several reasons, such as improving security and authentication, effectively verifying identities, providing personalized user experiences, and automating various operations, including attendance monitoring, access management, and law enforcement activities. In this paper, comprehensive evaluations are conducted using different face detection and modality segmentation methods, feature extraction methods, and classifiers to improve system performance. As for face detection, four methods are proposed: OpenCV’s Haar Cascade classifier, Dlib’s HOG + SVM frontal face detector, Dlib’s CNN face detector, and Mediapipe’s face detector. Additionally, two types of feature extraction techniques are proposed: hand-crafted features (traditional methods: global local features) and deep learning features. Three global features were extracted, Scale-Invariant Feature Transform (SIFT), Speeded Robust Features (SURF), and Global Image Structure (GIST). Likewise, the following local feature methods are utilized: Local Binary Pattern (LBP), Weber local descriptor (WLD), and Histogram of Oriented Gradients (HOG). On the other hand, the deep learning-based features fall into two categories: convolutional neural networks (CNNs), including VGG16, VGG19, and VGG-Face, and Siamese neural networks (SNNs), which generate face embeddings. For classification, three methods are employed: Support Vector Machine (SVM), a one-class SVM variant, and Multilayer Perceptron (MLP). The system is evaluated on three datasets: in-house, Labelled Faces in the Wild (LFW), and the Pins dataset (sourced from Pinterest) providing comprehensive benchmark comparisons for facial recognition research. The best performance accuracy for the proposed ten-feature extraction methods applied to the in-house database in the context of the facial recognition task achieved 99.8% accuracy by using the VGG16 model combined with the SVM classifier. Full article

Face recognition technology is a biometric technology, which is based on the identification or verification of facial features. Automatic face recognition is an active research field in the context of computer vision and artificial intelligence (AI) that is fundamental for a variety of real-time applications. In this research, the design and implementation of a face verification and identification system of a flexible, modular, secure, and scalable architecture is proposed. The proposed system incorporates several and various types of system components: (i) portable capabilities (mobile application and mixed reality [MR] glasses), (ii) enhanced monitoring and visualization via a user-friendly Web-based user interface (UI), and (iii) information sharing via middleware to other external systems. The experiments showed that such interconnected and complementary system components were able to perform robust and real-time results related to face identification and verification. Furthermore, to identify a proper model of high accuracy, robustness, and performance speed for face identification and verification tasks, a comprehensive evaluation of multiple face recognition pre-trained models (FaceNet, ArcFace, Dlib, and MobileNetV2) on a curated version of the ID vs. Spot dataset was performed. Among the models used, FaceNet emerged as a preferable choice for real-time tasks due to its balance between accuracy and inference speed for both face identification and verification tasks achieving AUC of 0.99, Rank-1 of 91.8%, Rank-5 of 95.8%, FNR of 2% and FAR of 0.1%, accuracy of 98.6%, and inference speed of 52 ms. Full article

In recent years, deep learning techniques have become increasingly prominent in face recognition tasks, particularly through the extraction and classification of face vectors. These vectors enable the inference of demographic attributes such as gender, age, and ethnicity. This study introduces a gender classification approach based solely on face vectors, avoiding the use of traditional machine learning algorithms. Face embeddings were generated using three popular models: dlib, ArcFace, and FaceNet512. For classification, the Average Neural Face Embeddings (ANFE) technique was applied by calculating distances between vectors. To improve gender recognition performance for Asian individuals, a new dataset was created by scraping facial images and related metadata from AsianWiki. The experimental evaluations revealed that ANFE models based on ArcFace achieved classification accuracies of 93.1% for Asian women and 90.2% for Asian men. In contrast, the models utilizing dlib embeddings performed notably lower, with accuracies dropping to 76.4% for women and 74.3% for men. Among the tested models, FaceNet512 provided the best results, reaching 97.5% accuracy for female subjects and 94.2% for males. Furthermore, this study includes a comparative analysis between ANFE and other commonly used gender classification methods. Full article

Facial soft tissue reconstruction is an important tool in forensic investigations, especially when conventional identification methods are unsuccessful. This paper presents a digital workflow for facial reconstruction and identity verification using computer vision techniques applied to two forensic cases. The first case involves a cold case from 1993, in which a manual reconstruction by Prof. Helmer was conducted in 1994. We digitally reconstructed the same individual using CAD software (Blender), enabling a direct comparison between manual and digital techniques. To date, the deceased remains unidentified. The second case, from 2021, involved a digitally reconstructed face that was later matched to a missing person through DNA analysis. Here, comparison material was available, including an official photograph. A police officer involved in the case noted a “striking resemblance” between the reconstruction and the photograph. To evaluate this subjective impression, we performed quantitative analyses using three face recognition models (Dlib-based method, VGG-Face, and GhostFaceNet). The models did not indicate significant similarity, highlighting a gap between human perception and algorithmic assessment. These findings suggest that current face recognition algorithms may not yet be fully suited to evaluating reconstructions, which tend to deviate in subtle but critical facial features. To achieve better facial recognition results, further research is required to generate more anatomically accurate and detailed reconstructions that align more closely with the sensitivity of AI-based identification systems. Full article

Traditional facial recognition is realized by facial recognition algorithms based on 2D or 3D digital images and has been well developed and has found wide applications in areas related to identification verification. In this work, we propose a novel live face detection (LFD) method by utilizing snapshot spectral imaging technology, which takes advantage of the distinctive reflected spectra from human faces. By employing a computational spectral reconstruction algorithm based on Tikhonov regularization, a rapid and precise spectral reconstruction with a fidelity of over 99% for the color checkers and various types of “face” samples has been achieved. The flat face areas were extracted exactly from the “face” images with Dlib face detection and Euclidean distance selection algorithms. A large quantity of spectra were rapidly reconstructed from the selected areas and compiled into an extensive database. The convolutional neural network model trained on this database demonstrates an excellent capability for predicting different types of “faces” with an accuracy exceeding 98%, and, according to a series of evaluations, the system’s detection time consistently remained under one second, much faster than other spectral imaging LFD methods. Moreover, a pixel-level liveness detection test system is developed and a LFD experiment shows good agreement with theoretical results, which demonstrates the potential of our method to be applied in other recognition fields. The superior performance and compatibility of our method provide an alternative solution for accurate, highly integrated video LFD applications. Full article

Drowsy driving contributes to over 6000 fatal incidents annually in the US, underscoring the need for effective, non-intrusive drowsiness detection. This study seeks to address detection challenges, particularly in non-standard head positions. Our innovative approach leverages computer vision by combining facial feature detection using Dlib, head pose estimation with the HOPEnet model, and analyses of the percentage of eyelid closure over time (PERCLOS) and the percentage of mouth opening over time (POM). These are integrated with traditional machine learning models, such as Support Vector Machines, Random Forests, and XGBoost. These models were chosen for their ability to process detailed information from facial landmarks, head poses, PERCLOS, and POM. They achieved a high overall accuracy of 86.848% in detecting drowsiness, with a small overall model size of 5.05 MB and increased computational efficiency. The models were trained on the National Tsing Hua University Driver Drowsiness Detection Dataset, making them highly suitable for devices with a limited computational capacity. Compared to the baseline model from the literature, which achieved an accuracy of 84.82% and a larger overall model size of 37.82 MB, the method proposed in this research shows a notable improvement in the efficiency of the model with relatively similar accuracy. These findings provide a framework for future studies, potentially improving sleepiness detection systems and ultimately saving lives by enhancing road safety. Full article

Drowsiness has been recognized as a leading factor in road accidents worldwide. Despite considerable research in this area, this paper aims to improve the precision of drowsiness detection specifically for long-haul travel by employing the Dlib-based facial feature detection algorithm. This study proposes two algorithms: a static and adaptive frame threshold. Both approaches utilize eye closure ratio (ECR) and mouth aperture ratio (MAR) parameters to determine the driver’s level of drowsiness. The static threshold method issues a warning when the ECR and/or MAR values reach specific thresholds. In this method, the ECR threshold is established at 0.15 and the MAR threshold at 0.4. The static threshold method demonstrated an accuracy of 89.4% and a sensitivity of 96.5% using 1000 images. The adaptive frame threshold algorithm uses a counter to monitor the number of consecutive frames that meet the drowsiness criteria before triggering a warning. Additionally, the number of consecutive frames required is adjusted dynamically over time to enhance detection accuracy and more accurately indicate a state of drowsiness. The adaptive frame threshold algorithm was tested using four 30 min videos, from a publicly available dataset achieving a maximum accuracy of 98.2% and a sensitivity of 64.3% with 500 images. Full article

As many countries face rapid population aging, the supply of manpower for caregiving falls far short of the increasing demand for care. Therefore, if the care system can continuously recognize and track the care recipient and, at the first sign of a fall, promptly analyze the image to accurately assess the circumstances of the fall, it would be highly critical. This study integrates the mobility of drones in conjunction with the Dlib HOG algorithm and intelligent fall posture analysis, aiming to achieve real-time tracking of care recipients. Additionally, the study improves and enhances the real-time multi-person action analysis feature of OpenPose to enhance its analytical capabilities for various fall scenarios, enabling accurate analysis of the approximate real-time situation when a care recipient falls. In the experimental results, the system’s identification accuracy for four fall directions is higher than that of Google Teachable Machine’s Pose Project training model. Particularly, there is the significant improvement in identifying backward falls, with the identification accuracy increasing from 70.35% to 95%. Furthermore, the identification accuracy for forward and leftward falls also increases by nearly 14%. Therefore, the experimental results demonstrate that the improved identification accuracy for the four fall directions in different scenarios exceeds 95%. Full article

IoT applications revolutionize industries by enhancing operations, enabling data-driven decisions, and fostering innovation. This study explores the growing potential of IoT-based facial recognition for mobile devices, a technology rapidly advancing within the interconnected IoT landscape. The investigation proposes a framework called IoT-MFaceNet (Internet-of-Things-based face recognition using MobileNetV2 and FaceNet deep-learning) utilizing pre-existing deep-learning methods, employing the MobileNetV2 and FaceNet algorithms on both ImageNet and FaceNet databases. Additionally, an in-house database is compiled, capturing data from 50 individuals via a web camera and 10 subjects through a smartphone camera. Pre-processing of the in-house database involves face detection using OpenCV’s Haar Cascade, Dlib’s CNN Face Detector, and Mediapipe’s Face. The resulting system demonstrates high accuracy in real-time and operates efficiently on low-powered devices like the Raspberry Pi 400. The evaluation involves the use of the multilayer perceptron (MLP) and support vector machine (SVM) classifiers. The system primarily functions as a closed set identification system within a computer engineering department at the College of Engineering, Mustansiriyah University, Iraq, allowing access exclusively to department staff for the department rapporteur room. The proposed system undergoes successful testing, achieving a maximum accuracy rate of 99.976%. Full article

Individuals with obstructive sleep apnea (OSA) face increased accident risks due to excessive daytime sleepiness. PERCLOS, a recognized drowsiness detection method, encounters challenges from image quality, eyewear interference, and lighting variations, impacting its performance, and requiring validation through physiological signals. We propose visual-based scoring using adaptive thresholding for eye aspect ratio with OpenCV for face detection and Dlib for eye detection from video recordings. This technique identified 453 drowsiness (PERCLOS ≥ 0.3 || CLOSDUR ≥ 2 s) and 474 wakefulness episodes (PERCLOS < 0.3 and CLOSDUR < 2 s) among fifty OSA drivers in a 50 min driving simulation while wearing six-channel EEG electrodes. Applying discrete wavelet transform, we derived ten EEG features, correlated them with visual-based episodes using various criteria, and assessed the sensitivity of brain regions and individual EEG channels. Among these features, theta–alpha-ratio exhibited robust mapping (94.7%) with visual-based scoring, followed by delta–alpha-ratio (87.2%) and delta–theta-ratio (86.7%). Frontal area (86.4%) and channel F4 (75.4%) aligned most episodes with theta–alpha-ratio, while frontal, and occipital regions, particularly channels F4 and O2, displayed superior alignment across multiple features. Adding frontal or occipital channels could correlate all episodes with EEG patterns, reducing hardware needs. Our work could potentially enhance real-time drowsiness detection reliability and assess fitness to drive in OSA drivers. Full article

The rapid expansion of e-commerce, particularly in the clothing sector, has led to a significant demand for an effective clothing industry. This study presents a novel two-stage image recognition method. Our approach distinctively combines human keypoint detection, object detection, and classification methods into a two-stage structure. Initially, we utilize open-source libraries, namely OpenPose and Dlib, for accurate human keypoint detection, followed by a custom cropping logic for extracting body part boxes. In the second stage, we employ a blend of Harris Corner, Canny Edge, and skin pixel detection integrated with VGG16 and support vector machine (SVM) models. This configuration allows the bounding boxes to identify ten unique attributes, encompassing facial features and detailed aspects of clothing. Conclusively, the experiment yielded an overall recognition accuracy of 81.4% for tops and 85.72% for bottoms, highlighting the efficacy of the applied methodologies in garment categorization. Full article

Detecting drowsiness among drivers is critical for ensuring road safety and preventing accidents caused by drowsy or fatigued driving. Research on yawn detection among drivers has great significance in improving traffic safety. Although various studies have taken place where deep learning-based approaches are being proposed, there is still room for improvement to develop better and more accurate drowsiness detection systems using behavioral features such as mouth and eye movement. This study proposes a deep neural network architecture for drowsiness detection employing a convolutional neural network (CNN) for driver drowsiness detection. Experiments involve using the DLIB library to locate key facial points to calculate the mouth aspect ratio (MAR). To compensate for the small dataset, data augmentation is performed for the ‘yawning’ and ‘no_yawning’ classes. Models are trained and tested involving the original and augmented dataset to analyze the impact on model performance. Experimental results demonstrate that the proposed CNN model achieves an average accuracy of 96.69%. Performance comparison with existing state-of-the-art approaches shows better performance of the proposed model. Full article

According to the survey statistics, most traffic accidents are caused by the driver’s behavior and status irregularities. Because there is no multi-level dangerous state grading system at home and abroad, this paper proposes a complex state grading system for real-time detection and dynamic tracking of the driver’s state. The system uses OpenMV as the acquisition camera combined with the cradle head tracking system to collect the driver’s current driving image in real-time dynamically, combines the YOLOX algorithm with the OpenPose algorithm to judge the driver’s dangerous driving behavior by detecting unsafe objects in the cab and the driver’s posture, and combines the improved Retinaface face detection algorithm with the Dlib feature-point algorithm to discriminate the fatigue driving state of the driver. The experimental results show that the accuracy of the three driver danger levels (R1, R2, and R3) obtained by the proposed system reaches 95.8%, 94.5%, and 96.3%, respectively. The experimental results of this system have a specific practical significance in driver-distracted driving warnings. Full article

The detection and evaluation of fatigue levels in drivers play a crucial role in reducing traffic accidents and improving the overall quality of life. However, existing studies in this domain often focus on fatigue detection alone, with limited research on fatigue level evaluation. These limitations include the use of single evaluation methods and relatively low accuracy rates. To address these issues, this paper introduces an innovative approach for determining fatigue driving levels. We employ the Dlib library and fatigue state detection algorithms to develop a novel method specifically designed to assess fatigue levels. Unlike conventional approaches, our method adopts a multi-feature fusion strategy, integrating fatigue features from the eyes, mouth, and head pose. By combining these features, we achieve a more precise evaluation of the driver’s fatigue state level. Additionally, we propose a comprehensive evaluation method based on the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation, combined with situational prediction. This approach effectively evaluates the fatigue state level of drivers at specific moments or stages and provides accurate predictions. Furthermore, we optimize the gated recurrent unit (GRU) network using an enhanced marine predator algorithm (MAP), which results in significant improvements in predicting fatigue levels during situational prediction. Experimental results demonstrate a classification accuracy of 92% across various scenarios while maintaining real-time performance. In summary, this paper introduces a novel approach for determining fatigue driving levels through multi-feature fusion. We also incorporate AHP-fuzzy comprehensive evaluation and situational prediction techniques, enhancing the accuracy and reliability of fatigue level evaluation. This research holds both theoretical and practical significance in the field of fatigue driving. Full article

Fatigued driving is one of the leading causes of traffic accidents, and detecting fatigued driving effectively is critical to improving driving safety. Given the variety and individual variability of the driving surroundings, the drivers’ states of weariness, and the uncertainty of the key characteristic factors, in this paper, we propose a deep-learning-based study of the MAX-MIN driver fatigue detection algorithm. First, the ShuffleNet V2K16 neural network is used for driver face recognition, which eliminates the influence of poor environmental adaptability in fatigue detection; second, ShuffleNet V2K16 is combined with Dlib to obtain the coordinates of driver face feature points; and finally, the values of EAR and MAR are obtained by comparing the first 100 frames of images to EAR-MAX and MAR-MIN. Our proposed method achieves 98.8% precision, 90.2% recall, and 94.3% F-Score in the actual driving scenario application. Full article