Face Data Research Articles

Event-related driver stress detection with smartphones in an urban environment: a naturalistic driving study

Driving in urban areas can be challenging and encounter acute stress. To detect driver stress, collecting data on real roads without interfering the driver is preferred. A smartphone-based data collection protocol was developed to support a naturalistic driving study. Sixty-one participants drove on predetermined real road routes, and driving information as well as physiological, psychological, and facial data were collected. The algorithm identified potentially stressful events based on the collected data. Participants classified these events as low, medium, or highly stressful events by watching recorded videos after the experiment. These events were then used to train prediction models. The best model achieved an accuracy of 92.5% in classifying low/medium/highly stressful events. The contribution of physiological, psychological, and facial expression indices and individual profile information was evaluated. The method can be applied to visualise the geographical distribution of stressors, monitor driver behaviour, and help drivers regulate their driving habits.

Facial mask-wearing prediction and adaptive gender classification using convolutional neural networks

The world has lived an exceptional time period caused by the Coronavirus pandemic. To limit Covid-19 propagation, governments required people to wear a facial mask outside. In facial data analysis, mask-wearing on the human face creates predominant occlusion hiding the important oral region and causing more challenges for human face recognition and categorisation. The appropriation of existing solutions by taking into consideration the masked context is indispensable for researchers. In this paper, we propose an approach for mask-wearing prediction and adaptive facial human-gender classification. The proposed approach is based on convolutional neural networks (CNNs). Both mask-wearing and gender information are crucial for various possible applications. Experimentation shows that mask-wearing is very well detectable by using CNNs and justifies its use as a prepossessing step. It also shows that retraining with masked faces is indispensable to keep up gender classification performances. In addition, experimentation proclaims that in a controlled face-pose with acceptable image quality' context, the gender attribute remains well detectable. Finally, we show empirically that the adaptive proposed approach improves global performance for gender prediction in a mixed context.

Three-Dimensional Virtual Reconstruction of External Nasal Defects Based on Facial Mesh Generation Network.

(1) Background: In digital-technology-assisted nasal defect reconstruction methods, a crucial step involves utilizing computer-aided design to virtually reconstruct the nasal defect's complete morphology. However, current digital methods for virtual nasal defect reconstruction have yet to achieve efficient, precise, and personalized outcomes. In this research paper, we propose a novel approach for reconstructing external nasal defects based on the Facial Mesh Generation Network (FMGen-Net), aiming to enhance the levels of automation and personalization in virtual reconstruction. (2) Methods: We collected data from 400 3D scans of faces with normal morphology and combined the structured 3D face template and the Meshmonk non-rigid registration algorithm to construct a structured 3D facial dataset for training FMGen-Net. Guided by defective facial data, the trained FMGen-Net automatically generated an intact 3D face that was similar to the defective face, and maintained a consistent spatial position. This intact 3D face served as the 3D target reference face (3D-TRF) for nasal defect reconstruction. The reconstructed nasal data were extracted from the 3D-TRF based on the defective area using reverse engineering software. The '3D surface deviation' between the reconstructed nose and the original nose was calculated to evaluate the effect of 3D morphological restoration of the nasal defects. (3) Results: In the simulation experiment of 20 cases involving full nasal defect reconstruction, the '3D surface deviation' between the reconstructed nasal data and the original nasal data was 1.45 ± 0.24 mm. The reconstructed nasal data, constructed from the personalized 3D-TRF, accurately reconstructed the anatomical morphology of nasal defects. (4) Conclusions: This paper proposes a novel method for the virtual reconstruction of external nasal defects based on the FMGen-Net model, achieving the automated and personalized construction of the 3D-TRF and preliminarily demonstrating promising clinical application potential.

SecureSense: Enhancing Person Verification through Multimodal Biometrics for Robust Authentication

Biometrics provide enhanced security and convenience compared to conventional methods of individual authentication. A more robust and effective method of individual authentication has emerged due to recent advancements in multimodal biometrics. Unimodal systems offer lower security and lack the robustness found in multimodal biometric systems. The research paper introduces a novel approach, employing multiple biometric modalities, including face, fingerprint, and iris, to authenticate users in a multimodal biometric system. The paper proposes the ”Secure Sense” framework, which combines multiple biometric modalities to improve person verification accuracy. The proposed system utilizes both web-based and real-time datasets. For the web-based dataset, we employed the Chicago Face dataset for facial data, the MMU1 dataset for iris data, and the SOCOfing dataset for fingerprint data. In real-time data collection, facial data is captured using a Zebronics Zeb-Gem webcam, fingerprint data is obtained using the Mantra MFS scanner, and iris data is collected using the Mantra MIS scanner. In the envisioned system, we introduce an innovative approach that employs a decision-level fusion technique across three unique biometric modalities, resulting in an impressive accuracy rate of approximately 93% across all modalities.

A Secure Face Verification Scheme Based on Fully Homomorphic Encryption with Anonymity

With the widespread adoption of cloud computing, the face verification process often requires the client to upload the face to an untrusted cloud server to obtain the verification results. Privacy leakage issues may arise if the client’s private information is not protected. This paper proposes a secure and anonymous face verification scheme using fully homomorphic encryption technology and SealPIR. Our scheme is a three-party solution that requires a third-party server trusted by the client. This scheme not only prevents the client’s facial data from being obtained by untrusted data servers but also prevents the data server from learning the index corresponding to the face that the client wants to verify. In a single-face verification process, the client only needs to perform one upload operation and one download operation, with a communication volume of 264 KB. We can complete a privacy-protected anonymous face verification process in 84.91 ms.

Air Traffic Controller Fatigue Detection by Applying a Dual-Stream Convolutional Neural Network to the Fusion of Radiotelephony and Facial Data

The role of air traffic controllers is to direct and manage highly dynamic flights. Their work requires both efficiency and accuracy. Previous studies have shown that fatigue in air traffic controllers can impair their work ability and even threaten flight safety, which makes it necessary to carry out research into how to optimally detect fatigue in controllers. Compared with single-modality fatigue detection methods, multi-modal detection methods can fully utilize the complementarity between diverse types of information. Considering the negative impacts of contact-based fatigue detection methods on the work performed by air traffic controllers, this paper proposes a novel AF dual-stream convolutional neural network (CNN) architecture that simultaneously extracts controller radio telephony fatigue features and facial fatigue features and performs two-class feature-fusion discrimination. This study designed two independent convolutional processes for facial images and radio telephony data and performed feature-level fusion of the extracted radio telephony and facial image features in the fully connected layer, with the fused features transmitted to the classifier for fatigue state discrimination. The experimental results show that the detection accuracy of radio telephony features under a single modality was 62.88%, the detection accuracy of facial images was 96.0%, and the detection accuracy of the proposed AF dual-stream CNN network architecture reached 98.03% and also converged faster. In summary, a dual-stream network architecture based on facial data and radio telephony data is proposed for fatigue detection that is faster and more accurate than the other methods assessed in this study.

Research on facial recognition system based on deep learning

This essay explores the integration of deep learning techniques in facial recognition, delving into the advancements, challenges, and prospects in future of this transformative technology. Deep learning, a subset of artificial intelligence, has revolutionized facial recognition by enabling automatic feature learning and robust representations from raw facial data. This paper examines the key components of deep learning in facial recognition, including feature extraction, facial alignment, and face recognition. It highlights the advantages of using deep learning over traditional methods by analysing related research. It also addresses the challenges deep learning faces in facial recognition, such as data bias, privacy concerns, and adversarial attacks. The essay also explores the promising future of deep learning in facial recognition, with ongoing research aiming to improve robustness, fairness, and privacy preservation.

Implementasi Sistem Keamanan Rumah Berbasis Pengenalan Wajah untuk Peningkatan Keamanan Residensial

The objective of this research is to develop a home security system that utilizes facial recognition technology using the Haar Cascade method and ESP32CAM. In the face of criminal threats, the importance of an effective home security system is increasing. The facial recognition method employed in this study is the Haar Cascade method, which utilizes specific facial features to identify individual identities. The ESP32CAM sensor is used as a camera to capture facial images of the home occupants. The research involves the process of collecting facial image data from the home occupants, which is then used to train the facial recognition model. Subsequently, the model is implemented in a home security system that is connected to the internet and equipped with motion sensors. When motion is detected, the ESP32CAM camera captures an image of the visitor's face and compares it with the previously identified facial data. If a match is found, the system grants access to the visitor. The research results demonstrate that the developed home security system achieves accurate facial recognition with a high success rate. Therefore, this system has the potential to enhance home security and provide better protection for its occupants

Face-based age and gender classification using deep learning model

The classification of the age and gender of face image data is a significant study subject with numerous applications. Convolution Neural Networks (CNNs) model is the effective technique to classify data, particularly for unrestricted real-world faces. In this current study, we introduce an innovative CNN-based method specifically designed for the extraction of distinguishing features from real-world facial data. These extracted features play a crucial role in the categorization and classification of images, with a focus on age and gender. We perform pre-training on facial images using the IMDb-WIKI dataset and subsequently classify both age and gender using unrestricted real-life facial data. To avoid over-fitting and enable the proposed model to generalise on the test images, we additionally chose a drop-out and data augmentation regularisation strategy. We demonstrate that optimised training hyper-parameters and well-planned network architecture produce better outcomes. The proposed technique achieved the 84.8 percent age group classification accuracy rate while the compared CNN2ELM model achieve the 52.3 percent accuracy rate. The suggested model demonstrates significantly better performance when contrast on the CNN2ELM model on the OIU-Adience dataset, especially in the context of age group classification and overall accuracy.

Toward robust and privacy-enhanced facial recognition: A decentralized blockchain-based approach with GANs and deep learning.

In recent years, the extensive use of facial recognition technology has raised concerns about data privacy and security for various applications, such as improving security and streamlining attendance systems and smartphone access. In this study, a blockchain-based decentralized facial recognition system (DFRS) that has been designed to overcome the complexities of technology. The DFRS takes a trailblazing approach, focusing on finding a critical balance between the benefits of facial recognition and the protection of individuals' private rights in an era of increasing monitoring. First, the facial traits are segmented into separate clusters which are maintained by the specialized node that maintains the data privacy and security. After that, the data obfuscation is done by using generative adversarial networks. To ensure the security and authenticity of the data, the facial data is encoded and stored in the blockchain. The proposed system achieves significant results on the CelebA dataset, which shows the effectiveness of the proposed approach. The proposed model has demonstrated enhanced efficacy over existing methods, attaining 99.80% accuracy on the dataset. The study's results emphasize the system's efficacy, especially in biometrics and privacy-focused applications, demonstrating outstanding precision and efficiency during its implementation. This research provides a complete and novel solution for secure facial recognition and data security for privacy protection.

Activation Functions Study for the Trustworthiness Supervisor Artificial Neural Networks

Examining and potentially adjusting one’s cognitive processes in response to dissatisfaction with one’s performance is a fundamental aspect of intelligence. Remarkably, such sophisticated abstract concepts necessary for achieving Artificial General Intelligence can be effectively incorporated into basic Machine Learning algorithms. In this study, we introduce a method for replicating self-awareness through a supervisory Artificial Neural Network (ANN), which monitors patterns in the activation functions of an underlying ANN to identify signs of substantial uncertainty within the underlying ANN and, consequently, the reliability of its predictions. The underlying ANN in this context is a Convolutional Neural Network (CNN) ensemble primarily utilized for tasks related to facial recognition and facial expression analysis. We evaluate the performance of the supervisory ANNs using various activation functions as they learn to gauge the dependability of predictions made by the Inception v3 CNN ensemble. To conduct computational experiments, we employ a facial data set that incorporates makeup and occlusion factors. These experiments are designed to mimic real-world conditions where the training data set exclusively consists of images without makeup or occlusion, while the test data set comprises images featuring makeup and occlusion. This partitioning ensures the model is tested under challenging out-of-training data distribution scenarios.

A Study on Autism Detection from Facial Images

Abstract: Autism is a serious type of neurodevelopmental condition that disrupts cognitive functioning, language, and social behavior. Individuals with autism spectrum disorders have a broad range of intellectual functioning, from significant disability to outstanding abilities. The severity and long-term repercussions of ASD can be avoided with an early diagnosis. Medical professionals currently use a number of methods to predict autism, including brain scan analysis, autism diagnostic interviews, autism diagnostic observations, and physical facial trait analysis. These traditional ways of diagnosing autism are quite timeconsuming, expensive, and complicated. People with autism have a distinctive set of facial traits which distinguish them from normal ones. One of the most interesting areas of autism study is the application of facial traits as a physical indicator for autism diagnosis. The use of deep learning and machine learning has become increasingly widespread in recent years, particularly in the field of image classification. These algorithms are capable of identifying hidden autism patterns from vast amounts of facial data, making them useful as autism predictor. Hence, this study reviews the various autism prediction methods based on facial features using deep learning and machine learning techniques.

Real-Time Visitor Counting with Dynamic Facial Recognition using Python and Machine Learning

Visitor data or the number of visitors at a particular location is crucial information to be obtained. This data can serve various purposes, particularly in enhancing customer satisfaction. For instance, predicting the number of visitors at tourist destinations enables tourism management to be better prepared for welcoming and providing optimal services to arriving visitors. Visitor count data can also be employed to automatically restrict visitors during the COVID-19 pandemic, ensuring a safe and comfortable environment with limited attendees. To acquire visitor data, a system capable of accurate visitor detection is required. This research utilizes computer vision to detect visitor faces. The developed system, programmed in Python, functions by detecting visitor faces and conducting a count based on the detected faces. To prevent the same visitor from being detected multiple times, a facial recognition method with dynamic facial data collection is implemented in this study. The constructed system successfully counted 27 out of 28 visitors over a two-day period. However, the system has limitations, particularly in terms of the restricted detection area. Therefore, a physical mechanism mandating visitors to undergo facial scanning and registration needs to be established, ensuring recorded data corresponds to the actual visitor count.

IDENTITY MANAGEMENT, SSI AND BLOCKCHAIN: A REVIEW

As an individual, in the social and digital world, the burden of managing multiple online IDs and passwords, while handling a host of documents e.g passports, driver’s licenses, Social Security/Aadhar cards and medical insurance cards is cumbersome. Identities both in the social and digital world requires security and privacy: managing these identities efficiently is called Identity Management. Once these identities established, they are produced, shown, send, verified in some way or the other for various processes in Government and non Government purposes. Lately it is established that Blockchain technology has immense use to control over identities (Identity Management) with complete secrecy and privacy. Self-sovereign identity (SSI) is undergoing a transformative evolution, driven by the integration of blockchain technology and Facial Recognition Technology (FRT). This envisioned future entails a paradigm shift where unique facial features serve as the secure key for unlocking one's identity. Blockchain, functioning as a decentralized and secure repository, facilitates the storage of facial data. This convergence of SSI, blockchain, and FRT not only augments security but also streamlines authentication processes, reducing the reliance on conventional passwords and identification documents. It’s a future where individuals can seamlessly access their data, marking a significant advancement in secure and personalized identity management within an academic context.

Deep learning-assisted construction of three-dimensional face midsagittal plane based on point clouds

Objective: To establish an intelligent registration algorithm under the framework of original-mirror alignment algorithm to construct three-dimensional(3D) facial midsagittal plane automatically. Dynamic Graph Registration Network (DGRNet) was established to realize the intelligent registration, in order to provide a reference for clinical digital design and analysis. Methods: Two hundred clinical patients without significant facial deformities were collected from October 2020 to October 2022 at Peking University School and Hospital of Stomatology. The DGRNet consists of constructing the feature vectors of key points in point original and mirror point clouds (X, Y), obtaining the correspondence of key points, and calculating the rotation and translation by singular value decomposition. Original and mirror point clouds were registrated and united. The principal component analysis (PCA) algorithm was used to obtain the DGRNet alignment midsagittal plane. The model was evaluated based on the coefficient of determination (R2) index for the translation and rotation matrix of test set. The angle error was evaluated on the 3D facial midsagittal plane constructed by the DGRNet alignment midsagittal plane and the iterative closet point(ICP) alignment midsagittal plane for 50 cases of clinical facial data. Results: The average angle error of the DGRNet alignment midsagittal plane and ICP alignment midsagittal plane was 1.05°±0.56°, and the minimum angle error was only 0.13°. The successful detection rate was 78%(39/50) within 1.50° and 90% (45/50)within 2.00°. Conclusions: This study proposes a new solution for the construction of 3D facial midsagittal plane based on the DGRNet alignment method with intelligent registration, which can improve the efficiency and effectiveness of treatment to some extent.

Research on facial recognition of sika deer based on vision transformer

In the face of global concerns about endangered ecosystems, it is vital to identify individual animals. Along these lines, in this work, a Vision Transformer (ViT) based model for sika deer individual recognition using facial data was designed. To get the satisfactory results, both low-level aspects like texture and color must also be considered, in addition to the high-level semantic information. Consequently, it was difficult to get good results by only applying advanced retrieval features. The standard ViT or ViT with ResNet (Residual neural network) as the backbone network may not be the best solution, as the direct patch flattening method of feature embedded in the conventional ViT is not applicable for performing deer face recognition. Therefore, DenseNet (Densely connected convolutional networks) block as Module 1 was used for extracting low-level features. DenseNet layers enable feature reuse through dense connections, and any layer can communicate directly. Thus maximum exchange of information flow between layers in the network is enabled. In Module 2, the mask approach was also used to eliminate extraneous information from the images and reduce interference from complicated backgrounds on the identification accuracy. In addition, the pixel multiplication of the feature map output from the two modules enabled the fusion of the local features with global features, enriching hence the expressiveness of the feature map. Finally, the ViT structure was run through pre-trained. The experimental results showed that the proposed model can reach an accuracy of 97.68% for identifying sika deer individuals and exhibited excellent generalization capabilities. A valid database for the individual identification of sika deer is provided by our work, significantly contributing to the conservation and promotion of the ecosystem.

Driver Emotion Recognition With a Hybrid Attentional Multimodal Fusion Framework

Negative emotions may induce dangerous driving behaviors leading to extremely serious traffic accidents. Therefore, it is necessary to establish a system that can automatically recognize driver emotions so that some actions can be taken to avoid traffic accidents. Existing studies on driver emotion recognition have mainly used facial data and physiological data. However, there are fewer studies on multimodal data with contextual characteristics of driving. In addition, fully fusing multimodal data in the feature fusion layer to improve the performance of emotion recognition is still a challenge. To this end, we propose to recognize driver emotion using a novel multimodal fusion framework based on convolutional long-short term memory network (ConvLSTM), and hybrid attention mechanism to fuse non-invasive multimodal data of eye, vehicle, and environment. In order to verify the effectiveness of the proposed method, extensive experiments have been carried out on a dataset collected using an advanced driving simulator. The experimental results demonstrate the effectiveness of the proposed method. Finally, a preliminary exploration on the correlation between driver emotion and stress is performed.

Online Attendance with Python Face Recognition and Django Framework

Online learning certainly requires an attendance system that is accessed anywhere with a minimum level of fraud. This research aimed to build an online attendance system using face recognition to prevent filling out online learning attendance represented by others. The online attendance system was built using the object-based system approach method. The system development method used was the waterfall. The development of this system utilizes the Django python framework, face recognition library, and OpenCV. This research delivered an attendance system that could not be represented by others. To record attendance, students visit the online attendance system. Students can only record attendance once according to the lecture schedule. The camera will capture the student’s face and equate it with the existing facial data. Only the registered student that his attendance data stored in the database. In addition, students cannot record attendance with face recognition outside of their lecture hours. This attendance system was tested using black-box testing. The test is carried out on the access button function to record attendance during the lecture schedules data and outside the lecture schedule, facial recognition function with valid and not valid facial data, function to store attendance data, and function to view attendance data recap in the current semester. The result showed that the attendance application with facial recognition is 100% running as it should and as expected.

CNN Based Efficient Facial Emotion Recognition System for Children

An efficient and robust technique for facial expression analysis for the different human face image input is carried out. During the analysis with every dataset, it is analysed through the Convolution Neural network (CNN) method. In order to analyse the facial data in terms of emotions, at this stage of research it is considered with the three different emotions such as Happy, Sad and Anger. For the analysis of facial data for the very rarely occurring disorder autism disorder in the age group around 10 years termed as children age group. As an initial step of analysis of autism disorder emotion expressed through face is a significant symptom hence in the present research is carried out using the freely available Kaggle database to achieve the better accuracy and stability in the obtained results by the proposed technique of methodology. To show the proposed technique is better than the existing algorithms, two different database are considered and cross correlation among the database in terms of its efficiency are considered.

Personalized 3D Printed Eye Gear for Microscopic Surgeons Amidst and beyond COVID-19.

The COVID-19 pandemic underscored the need for enhanced protective measures for healthcare workers, particularly surgeons, who face a heightened risk of exposure to infectious aerosols. However, conventional eye protection equipment such as face shields, goggles, or glasses often leads to ergonomic discomfort and a reduced field of view (FOV), impeding surgeons' ability to perform microsurgical procedures with precision and ease. To address these limitations, this study aimed to develop personalized 3D-printed eye gear for microscopic surgeons based on facial anthropometry data. 3D scanning was employed to obtain facial data from ten neurosurgery residents. Utilizing computer-aided designing, eye gears tailored to the unique facial features of each participant were developed. Finite element analysis-based contact simulation was used to assess the pressure exerted by the eye gear. Multi-material 3D printing was employed to fabricate the personalized eye gear. Participants, while donning the eye gear, engaged in simulation-based micro suturing tasks at various magnifications of the operating microscope, and marked the FOV range. They provided feedback scores (1-10) on the effectiveness of the eye gear through a Likert scale questionnaire (Q1-Q8). Finite element analysis demonstrated uniform strain distribution on the face, indicating that the edges of the customized eye gear fit exactly to the user's face. The average scores for the questionnaire Q1 to Q8 ranged from 6.8 to 8.5, with an overall mean score of 7.6. This indicates that the developed eye gear was simple to use and did not cause any discomfort. Additionally, the average reduction in the FOV was only 10.93% across the different operating microscope magnifications. These findings highlight eye gear's potential to alleviate discomfort and enhance precision in microscopic surgeries. Consequently, personalized 3D-printed eye gear offers a promising solution for providing surgeons with a safe environment while preserving the benefits of the operating microscope.