Face Orientation Research Articles

New method to identify optimal discontinuity set number of rock tunnel excavation face orientation based on Fisher mixed evaluation

Discontinuity is critical for strength, deformability, and permeability of rock mass. Set information is one of the essential discontinuity characteristics and is usually accessed by orientation grouping. Traditional methods of identifying optimal discontinuity set numbers are usually achieved by clustering validity indexes, which mainly relies on the aggregation and dispersion of clusters and leads to the inaccuracy and instability of evaluation. This paper proposes a new method of Fisher mixed evaluation (FME) to identify optimal group numbers of rock mass discontinuity orientation. In FME, orientation distribution is regarded as the superposition of Fisher mixed distributions. Optimal grouping results are identified by considering the fitting accuracy of Fisher mixed distributions, the probability monopoly and central location significance of independent Fisher centers. A Halley-Expectation-Maximization (EM) algorithm is derived to achieve an automatic fitting of Fisher mixed distribution. Three real rock discontinuity models combined with three orientation clustering algorithms are adopted for discontinuity grouping. Four clustering validity indexes are used to automatically identify optimal group numbers for comparison. The results show that FME is more accurate and robust than the other clustering validity indexes in optimal discontinuity group number identification for different rock models and orientation clustering algorithms.

Aerial display that reconstructs face orientation by use of spatial blending of two face images

AbstractThis paper proposes a novel aerial display system that reconstructs face orientation. The proposed system forms two face images floating in mid‐air. Viewers observe a spatially blended image of the two face images, where the spatial blending ratio depends on the viewing position. Thus, the spatially blended aerial face image is perceived to look in a fixed orientation even if the viewing position is changed within a certain viewing range. We analyze the spatial blending system optical design and show results from our prototype display.

Face orientation recognition based on Kinectv2 and an improved ELM model

Abstract Face orientation recognition is a crucial component of human behavior analysis. Challenges such as face occlusion and varying lighting conditions frequently degrade recognition accuracy. This study introduces an innovative approach using the Kinect sensor’s skeleton tracking technology to capture face orientation images across different lighting scenarios. To enhance image quality, techniques such as light attenuation and compensation are applied. Furthermore, multiple feature vectors based on face orientation are extracted and fused, followed by the optimization of the Extreme Learning Machine (ELM) model through the Archimedean Optimization Algorithm (AOA). The developed AOA-ELM model for face orientation recognition is compared with traditional models, including Back Propagation (BP), Radial Basis Function (RBF), standard ELM, and Genetic Algorithm enhanced ELM (GA-ELM). The AOA-ELM model exhibits superior performance in estimating face orientation amidst lighting disturbances, achieving an accuracy exceeding 95% in varied lighting conditions, thereby outperforming other classification models.

Enhancing Academic Integrity in Online Assessments: Introducing an Effective Online Exam Proctoring Model using YOLO

The issue of cheating during exams has become a significant concern, gaining widespread attention from the public and media. This issue has been made worse by the transition of educational institutions to an online format, making it simpler to engage in academic dishonesty. To overcome this problem, developed an innovative solution known as the Online Exam Proctoring Model using You Only Look Once (YOLO) Model. With the use of cutting-edge computer vision algorithms, the proposed model makes use of data from cameras and microphones to automatically spot and prevent cheating during online exams. Face orientation, face spoofing detection, mouth tracking, audio analysis, mobile device recognition, and person counting are among the primary elements built into the model. Object detection algorithms, in conjunction with pre-trained YOLO weights, empower the model to detect mobile devices and count the number of individuals present in the exam environment. The automatic termination of exams upon detection of mobile devices, multiple persons, or no persons for 10 seconds acts as a strong deterrent against cheating. The suggested methodology created a reliable and effective tool for online examinations, guaranteeing the academic integrity and enabling impartial assessment of the skills and knowledge of learners. By implementing this solution, educational institutions can foster a secure online exam environment, promoting a culture of honesty and trust between students and educators. As navigate the evolving environment of education, the model stands as a reliable ally in upholding the credibility and authenticity of online learning experiences.

Vision Transformer Based Photo Capturing System

Portrait photo is one of the most crucial documents that many people need for official transactions in many public and private organizations. Despite the developing technologies and high resolution imaging devices, people need such photographer offices to fulfil their needs to take photos. In this study, a Photo Capturing System has been developed to provide infrastructure for web and mobile applications. After the system detects the person's face, facial orientation and facial expression, it automatically takes a photo and sends it to a graphical user interface developed for this purpose. Then, with the help of the user interface of the photo taken by the system, it is automatically printed out. The proposed study is a unique study that uses imaging technologies, deep learning and vision transformer algorithms, which are very popular image processing techniques in several years. Within the scope of the study, face detection and facial expression recognition are performed with a success rate of close to 100\% and 95.52\%, respectively. In the study, the performances of Vision Transformer algorithm is also compared with the state of art algorithms in facial expression recognition.

Experimental investigation of heat transfer characteristics in a miniature flat heat pipe with multi-channels

The heat transfer characteristics of a miniatured flat heat pipe (MFHP) with multi-channels, featuring a port diameter of 1.18 mm, is investigated experimentally. Various operating parameters are considered, including the working fluid volume (Vf = 1.5, 2.5, and 3.5 ml), length of the liquid reservoir (Lres = No reservoir, 5, and 10 mm), orientation such as axial face (αa) or lateral side (αl), inclination angles (α = −15 to 90o), and cooling water flow rates (ṁi = 10, 15, and 20 LPH). Based on the experiments, the optimal values for the working fluid volume, reservoir length, and flow rate are determined as Vf = 2.5 ml, Lres = 5 mm, and ṁi = 20 LPH, respectively. Further analysis reveals that, the heat dissipation rate for the axial face is significantly higher than that of the lateral side, with an average percentage increase of 35.4 %. However, the lateral side outperforms the axial face in terms of stabilizing the evaporator wall temperature, reducing fluctuations by an average of 24.5 %. Moreover, the presence of multi-channels allows the MFHP in axial face orientation to dissipate a maximum heat load of 15 W against gravity at an inclination angle of αa = −15o. Finally, the variations in MFHP operation based on the orientation and its underlying physical mechanisms that contribute to enhancing heat transfer are discussed.

Analysis of SARS-CoV-2 transmission in airports based on real human close contact behaviors

The COVID-19 pandemic has significantly impacted people's daily lives for over three years. Airports, with their dense population and frequent close contact, pose a higher risk of respiratory infectious diseases compared to many other indoor environments. However, limited availability of data on close contact behavior has resulted in a gap in indoor exposure analysis. This study conducted depth sensor measurements and video data collection across nine areas of a northern (airport A) and a southern (airport B) airports in China by 11 participants. The data, comprising more than 44 h of close contact behaviors, including interpersonal distance, relative facial orientation, and the relative position of individuals, were analyzed using a semi-supervised machine learning method. Based on this analysis, a close contact transmission model for COVID-19 was developed, which considers the aforementioned close contact behaviors to assess the risk of exposure and the efficacy of interventions. The average close contact ratio in 9 airport's areas is 25.4 % (ranging from 6.1 % to 55.0 %), with passengers having the highest frequency of close contact in manual check-in areas. During close contacts, the average interpersonal distance in airports is 1.2 m (ranging from 1.1 to 1.4 m), being shortest in boarding areas. Face-to-face close contact is highest in charging areas, with a percentage of 46.9 %. If people maintain a distance of over 1.0 m in all areas, the total virus exposure could be reduced by 6.9 %–22.0 % compared to the actual situation. Dining areas have the highest virus exposure risk for both short-range inhalation and mucosal deposition, followed by manual check-in areas. This study provides a data support for the scientific epidemic prevention and control in airports from the viewpoint of close contact behaviors.

An Atlas of the base inter-RNA stacks involved in bacterial translation

Nucleobase-specific noncovalent interactions play a crucial role in translation. Herein, we provide a comprehensive analysis of the stacks between different RNA components in the crystal structures of the bacterial ribosome caught at different translation stages. Analysis of tRNA||rRNA stacks reveals distinct behaviour; both the A-and E-site tRNAs exhibit unique stacking patterns with 23S rRNA bases, while P-site tRNAs stack with 16S rRNA bases. Furthermore, E-site stacks exhibit diverse face orientations and ring topologies—rare for inter-chain RNA interactions—with higher average interaction energies than A or P-site stacks. This suggests that stacking may be essential for stabilizing tRNA progression through the E-site. Additionally, mRNA||rRNA stacks reveal other geometries, which depend on the tRNA binding site, whereas 16S rRNA||23S rRNA stacks highlight the importance of specific bases in maintaining the integrity of the translational complex by linking the two rRNAs. Furthermore, tRNA||mRNA stacks exhibit distinct geometries and energetics at the E-site, indicating their significance during tRNA translocation and elimination. Overall, both A and E-sites display a more diverse distribution of inter-RNA stacks compared to the P-site. Stacking interactions in the active ribosome are not simply accidental byproducts of biochemistry but are likely invoked to compensate and support the integrity and dynamics of translation.

Similarity Between Upright and Inverted Faces in a Yes/No Procedure: The Face Recognition Invariance

Abstract A 180° transformation in face orientation from the study stage to the test stage in a yes/no procedure reduces face recognition. Recognition of an upright face in the study and test stages (upright–upright [UU] group) is better than in any other groups (upright–inverted [UI], IU, and II). In contrast to these findings, we report a phenomenon wherein the same pattern of face recognition is obtained in all four groups (UI, UU, IU, and II), which we call face recognition invariance. This can be explained by the hypothesis of visual similarity between an inverted face and an upright face, not by the hypothesis of mental rotation from inverted face to upright face. Visual similarity is based on certain elements that are mutual to 2 faces and elements that differentiate them. These mutual and differentiating elements resist the transformation of inversion (a 180° transformation) and can therefore be considered symmetric or salient components of the face, such as round eyes or thick lips. Hence, the similarity between inverted face and upright face is an important factor in explaining the effect of inversion on face recognition.

Neurocognitive Adaptations for Spatial Orientation and Navigation in Astronauts.

Astronauts often face orientation challenges while on orbit, which can lead to operator errors in demanding spatial tasks. In this study, we investigated the impact of long-duration spaceflight on the neural processes supporting astronauts' spatial orientation skills. Using functional magnetic resonance imaging (fMRI), we collected data from 16 astronauts six months before and two weeks after their International Space Station (ISS) missions while performing a spatial orientation task that requires generating a mental representation of one's surroundings. During this task, astronauts exhibited a general reduction in neural activity evoked from spatial-processing brain regions after spaceflight. The neural activity evoked in the precuneus was most saliently reduced following spaceflight, along with less powerful effects observed in the angular gyrus and retrosplenial regions of the brain. Importantly, the reduction in precuneus activity we identified was not accounted for by changes in behavioral performance or changes in grey matter concentration. These findings overall show less engagement of explicitly spatial neurological processes at postflight, suggesting astronauts make use of complementary strategies to perform some spatial tasks as an adaptation to spaceflight. These preliminary findings highlight the need for developing countermeasures or procedures that minimize the detrimental effects of spaceflight on spatial cognition, especially in light of planned long-distance future missions.

Preliminary colour characterisation of a Stratasys J750 digital anatomy printer with different fillings and face orientations

Multicolour capability in additive manufacturing could play a key role in certain applications such as surgical training and consumer products. However, the ability to accurately 3D print colours is not well documented and could affect the realism of models produced through these technologies. As a recent system, the Stratasys J750 Digital Anatomy Printer has yet to be analyzed for its colour perception and accuracy, which is quantified through this study. This will allow users of this and similar material jetting systems with an improved understanding of the relationship between digitally applied colours and their result when 3D printed, as well as the influence of certain settings. Thirty-three rectangular prism models with different CMYK and RGB colours, as well as infill materials, were printed on a Stratasys J750 DAP printer. These were scanned on five faces using a Nix Mini 2 handheld colour sensor, documenting readings in CIELAB format. The data were analyzed using the CIEDE2000 colour difference formula, and its recent modifications for 3D printed objects. Results found statistically significant and perceptive differences in colour accuracy among different colours, core materials, and face orientations. It was also observed that the addition of VeroPureWhite as filler material instead of the default SUP706 support improved colour accuracy. The study recommends the following steps to improve colour accuracy: (i) avoid the addition of black (K) manually in CMYK colour space, (ii) use pure white as the base infill material instead of support material, (iii) add a little white (~ 10%–30%) to make samples opaque instead of translucent.

The effect of face orientation on audiovisual speech integration in infancy: An electrophysiological study.

Humans pay special attention to faces and speech from birth, but the interplay of developmental processes leading to specialization is poorly understood. We investigated the effects of face orientation on audiovisual (AV) speech perception in two age groups of infants (younger: 5- to 6.5-month-olds; older: 9- to 10.5-month-olds) and adults. We recorded event-related potentials (ERP)in response to videos of upright and inverted faces producing /ba/ articulation dubbed with auditory syllables that were either matching /ba/ or mismatching /ga/ the mouth movement. We observed an increase in the amplitude of audiovisual mismatch response (AVMMR) to incongruent visual /ba/-auditory /ga/ syllable in comparison to other stimuli in younger infants, while the older group of infants did not show a similar response.AV mismatch response to inverted visual /ba/-auditory /ga/ stimulus relative to congruent stimuli was also detected in the right frontal areas in the younger group and the left and right frontal areas in adults. We show that face configuration affects the neural response to AV mismatch differently across all age groups. The novel finding of the AVMMR in response to inverted incongruent AV speech may potentially imply the featural face processing in younger infants and adults when processing inverted faces articulating incongruent speech. The lack of visible differential responses to upright and inverted incongruent stimuli obtained in the older group of infants suggests a likely functional cortical reorganization in the processing of AV speech.

Visualization of Interest Level using OpenPose with Class Videos

The approach from the teacher's side to improve the classroom is to establish a learning discipline and an improvement method. However, there are various ways to do this, and it is an important part of teachers' work. In this paper, we propose a system for estimating the level of interest from class videos as a means of knowing the learning status of individual students. Using OpenPose, the system detects a person in a class video and extracts feature data of his/her joints. And we measure the concentration level based on the information of posture, facial orientation, and other movements. In addition, assuming that a person's face is facing the direction of the target when he/she is concentrating, we measure the concentration level based on the information of posture, facial direction, and other movements.

Face Recognition and Eye Blink Attendance Management System

This research explores the implementation and advantages of utilizing facial recognition technology for automated student attendance management in educational environments. The study outlines a comprehensive system that employs a high-resolution digital camera to detect and recognize students' faces, comparing them with stored images in a database. Successful matches result in real-time attendance logging, enhancing record-keeping and future analysis. The system also accommodates the inclusion of new images when no match is found. By embracing deep learning advancements, the technology offers an efficient alternative to conventional roll-call procedures, enabling automatic head counting in classrooms. The system integrates various face recognition algorithms, leveraging attributes such as shape, color, Local Binary Pattern (LBP), wavelet transforms, and auto-correlation. While challenges like varying lighting conditions, facial expressions, and face orientation are acknowledged, this research underscores the potential of technology to revolutionize attendance management and student engagement in education. Key Words: facial recognition technology, student attendance, automated system, deep learning, real-time logging, attendance management, classroom headcount, face recognition algorithms, record-keeping.

The generality of the attentional boost effect for famous, unfamiliar, and inverted faces.

Familiarity and face inversion not only affect face recognition and memory but also influence attention. Face processing is less attention-demanding for familiar than for unfamiliar faces and for upright than for inverted faces. The automaticity raises the question of how face processing interacts with an increase in attention. Using a dual-task paradigm, we tested the interaction between attention and face familiarity and orientation. Participants encoded a series of faces to memory while simultaneously monitoring a stream of colored squares, pressing the space bar for target-colored squares and making no response to distractor-colored squares. Replicating previous findings of the attentional boost effect (ABE), we found that faces encoded with target squares were better remembered than faces encoded with distractor squares. If the automatic nature of familiar (or upright) face processing makes attention unnecessary, then the attentional boost should be attenuated for familiar relative to unfamiliar faces and for upright relative to inverted faces. Data from three experiments showed, however, that the ABE was the same for all types of faces. These results suggest that target detection did not simply elevate attention in an early encoding phase. Rather, selecting targets and rejecting distractors in the color task may have led to yoked temporal selection of target-concurrent faces for entry into memory.

Bottlenose dolphins are sensitive to human attentional features, including eye functionality

The ability to attribute attentional states to other individuals is a highly adaptive socio-cognitive skill and thus may have evolved in many social species. However, whilst humans excel in this ability, even chimpanzees appear to not accurately understand how visual attention works, particularly in regard to the function of eyes. The complex socio-ecological background and socio-cognitive skill-set of bottlenose dolphins (Tursiops sp.), alongside the specialised training that captive dolphins typically undergo, make them an especially relevant candidate for an investigation into their sensitivity to human attentional states. Therefore, we tested 8 bottlenose dolphins on an object retrieval task. The dolphins were instructed to fetch an object by a trainer under various attentional state conditions involving the trainer’s eyes and face orientation: ‘not looking’, ‘half looking’, ‘eyes open’, and ‘eyes closed’. As the dolphins showed an increased latency to retrieve the object in conditions where the trainer’s head and eyes cued a lack of attention to the dolphin, particularly when comparing ‘eyes open’ vs ‘eyes closed’ conditions, we demonstrate that dolphins can be sensitive to human attentional features, namely the functionality of eyes. This study supports growing evidence that dolphins possess highly complex cognitive abilities, particularly those in the social domain.

Face-tracking algorithm for large-amplitude head motions with a 7-DOF manipulator

AbstractThe collection of facial action data is essential for the accurate evaluation of a patient’s condition in the intensive care unit, such as pain evaluation. An automatic face-tracking system is demanded to reduce the burden of data collection on the medical staff. However, many previous studies assume that the optimal trajectory of a robotic tracking system is reachable which is inapplicable for large-amplitude head motions. To tackle this problem, we propose a region-based face-tracking algorithm for large-amplitude head motion with a 7-DOF manipulator. A configuration-based optimization algorithm is proposed to trade-off between theoretical optimal pose and workspace constraints through the assignment of importance weights. To increase the probability of recapturing the face exceeding the reachable workspace of the manipulator, the camera is directed toward the center of the head, named the facial orientation center (FOC) constraint. Furthermore, a region-based tracking approach is designed to stabilize the manipulator for small amplitude head motions and smooth the tracking trajectory by adjusting the joint angle in the null space of the 7-DOF manipulator. Experimental results demonstrate the effectiveness of the proposed algorithm in tracking performance and finding an appropriate configuration for the unreachable theoretical optimal configuration. Moreover, the proposed algorithm with FOC constraint can successfully follow the head motion as losing 33.2% of the face during the tracking.

Automated quantification of glenoid bone loss in CT scans for shoulder dislocation surgery planning.

Estimation of glenoid bone loss in CT scans following shoulder dislocation is required to determine the type of surgery needed to restore shoulder stability. This paper presents a novel automatic method for the computation of glenoid bone loss in CT scans. The model-based method is a pipeline that consists of four steps: (1) computation of an oblique plane in the CT scan that best matches the glenoid face orientation; (2) selection of the glenoid oblique CT slice; (3) computation of the circle that best fits the posteroinferior glenoid contour; (4) quantification of the glenoid bone loss. The best-fit circle is computed with newly defined Glenoid Clock Circle Constraints. The pipeline and each of its steps were evaluated on 51 shoulder CT scans (44 patients). Ground truth oblique slice, best-fit circle, and glenoid bone loss measurements were obtained manually from three clinicians. The full pipeline yielded a mean absolute error (%) for the bone loss deficiency of 2.3 ± 2.9mm (4.67 ± 3.32%). The mean oblique CT slice selection difference was 1.42 ± 1.32 slices, above the observer variability of 1.74 ± 1.82 slices. The glenoid bone loss deficiency measure (%) on the ground truth oblique glenoid CT slice has a mean average error of 0.54 ± 1.03mm (4.76 ± 3.00%), close to the observer variability of 0.93 ± 1.40mm (2.98 ± 4.97%). Our pipeline is the first fully automatic method for the quantitative analysis of glenoid bone loss in CT scans. The computed glenoid bone loss report may assist orthopedists in selecting and planning surgical shoulder dislocation procedures.

The Study of Performance for Face Detection Based on Multiple Representative Convolutional Neural Networks

Due to the diversity of deep learning models, choosing the suitable model for a specific task can be rather onerous. In this paper, the performance of three deep convolutional neural networks, namely VGG16, ResNet50, and MobileNetV2 on face detection were compared. Each model was trained on a dataset of 11,900 images from the FDDB dataset that included various face sizes and orientations with multiple augmentations, including color alteration, blurring, and flipping. The final layers of the models were modified into a binary classification model and a regression model indicating face found and coordinates of the facial bounding box. The models were trained on the same basis of 40 epochs with batch size 64 with binary cross entropy loss and DIoU loss and a learning rate of 0.0001 with a learning rate decay of 0.8 per epoch. The experimental results demonstrated that VGG16 outperformed ResNet50 and MobileNetV2 in terms of accuracy, with VGG16 achieving the highest score of 0.9240, followed by ResNet50 with a score of 0.8568, and MobileNetV2 with an accuracy of 0.6028. The results suggest that VGG16 is a more suitable choice for face detection applications than ResNet50 and MobileNetV2, while ResNet50 and MobileNetV2 may provide higher accuracy for other image recognition tasks or real time face detections. The findings in this paper can contribute to the selection of appropriate deep learning models for face detection.

Coastal Dynamics Analysis Based on Orbital Remote Sensing Big Data and Multivariate Statistical Models

As the interface between land and water, coastlines are highly dynamic and intricately tied to the sediment budget. These regions have a high functional diversity and require enlightened management to preserve their value for the future. In this study we assess changes to the São Paulo State (SE Brazil) coastline over the last 36 years. The study innovatively employs big data remote sensing techniques and multivariate statistical models to evaluate and generate erosion/accretion rates (1985–2021) relative to beach orientation and slope. Shoreline change rates have been obtained for sandy beaches at 485 one-kilometer-spaced transects. Our findings capture the complexity and heterogeneity of the analyzed coastline, at a regional and local scale. No association was found between shoreline changes and beach face orientation. Nonetheless, a dependency relationship was found between dissipative beaches with moderate to high accretion. Beaches facing south, with relative stability, were prone to sediment accumulation. Locations with slow accretion, like sandy spits and tombolo-protected beaches, were associated with dissipative beaches with moderate to high accretion. The southeast-oriented beaches are more prone to erosion due to storm waves from the south. Results provide a broad, fast, and relatively low-cost methodology that can be used in any sandy beach context, bringing essential information for coastal management and decision-making related to the use and occupation of the coastal zones.