Eye Region Research Articles

The “Reading the Mind in Films” Task: A Pilot Study on Complex Emotion and Mental State Recognition for the Italian Adaptation in Adults with and Without Autism Spectrum Conditions

Background/Objectives: The present pilot study tested and reports the Italian adaptation of the Reading the Mind in Film test (RMF), an ecological test for assessing, in Italian adults with and without Autism Spectrum Condition (ASC), complex emotion and mental state recognition in natural settings and everyday situations. Method: A sample of young adults with Autism Spectrum Condition (with ASC; n = 22), attending a filmmaking course at a post-diploma school (Scuola Futuro Lavoro) took part in the study and was compared with a control group of neurotypical university students (without ASC; n = 22). All participants underwent individual testing and completed the Italian version of the Autism Questionnaire before performing the Italian version of both the RMF task and the Reading the Mind in the Eyes Test (RMET). The latter, widely used to evaluate the ability to detect what someone else is thinking or feeling from the eye region. Results: The findings of the control group were in line with the original study, demonstrating the validity and reliability of the translation and the dubbing procedure of the RMF test. However, no main significant differences in performance were found between the two groups. Conclusions: Such results suggest that taking a course in film and video making may have helped the autistic students learn how to recognize mental states.

Thinning of central foveal thickness in the retina of patients with schizophrenia.

The neural retina shares a common embryonic origin with the brain and yields pathological changes like that in the brain in various neuropsychiatric disorders, including schizophrenia. Non-invasive examination by optical coherence tomography (OCT) revealed retinal structure abnormalities in patients with schizophrenia. This study investigated retina structures in 29 patients with schizophrenia and 25 healthy controls in a Chinese Han ethnic population with spectral domain OCT. Measurements of central foveal thickness (CFT) as well as other retinal structures in macular and peripapillary subregions were performed. Associations between OCT parameters and clinical features, including severity of positive and negative symptoms, disease duration and antipsychotic dosage were analyzed. With controlling for age and sex, patients showed significantly thinner CFT, thinner central macular thickness, and thinner total retinal thickness in subregions of the central, inner superior, inner temporal, and inner nasal macula of both eyes, thinner ganglion cell complex in a subregion of the left eye, as well as larger cup volume in the peripapillary region of the right eye. In addition, CFT also negatively correlated with severity of negative symptoms. These findings suggest that CFT has the potential to be a disease biomarker of schizophrenia.

Social attention in the wild - fixations to the eyes and autistic traits during a naturalistic interaction in a healthy sample

Attention to social stimuli is a key component of social behavior and facilitates the development of fundamental social skills. Studies investigating social attention in neurotypical or neurodiverse populations have often relied on screen-based experiments using static images or videos, which lack the sensory richness and reciprocity present in real-life social interactions. This can possibly be attributed to the challenges one encounters when creating naturalistic experiments, such as dealing with dynamically moving areas of interest (AOIs), which require either time-intensive manual coding or restraining of participants. Here, we present findings from an experimental paradigm using unrestrained mobile eye-tracking and a face detection algorithm (MTCNN) to measure fixation rates during a semi-structured, face-to-face interview. Data from N = 62 healthy adult participants was analyzed for gaze behavior and related to participants’ autistic traits. We observed a significant negative correlation between fixation rates on the eye region averaged over the entire interaction and scores on the autism spectrum quotient (AQ) (r = −0.14), indicating participants with high autistic traits fixated less frequently on the eye region. We also compared different types of interview questions (open vs. closed) to explore whether the reduction in fixation rates was more pronounced for specific time intervals during the interview. Lastly, we discuss both possibilities for extensions as well as limitations of the presented paradigm that could serve as inspiration for future research.

Lack of agreement among analysers of infrared thermal images in the temperature of eye regions in sheep

Infrared thermography (IRT) is a non-invasive method to estimate body temperature in many animal species. The eye has been indicated as the ideal region for IRT, potentially serving as an early indicator of variations in body temperature triggered by various events and stimuli. Studies are being conducted to understand sources of variation in IRT temperature of the eye and develop comprehensive guidelines for veterinary medicine and animal production purposes. The present study adds information about variation in temperature estimated from different eye regions in sheep due to analysers. Thirty-one thermal images, fifteen of the right and sixteen of the left eyes of a ewe, captured with a FLIR E8-XT camera, were analysed by each of three analysers, and minimum (Tmin), maximum (Tmax) and average (Tavg) temperature in lacrimal caruncle (R1), medial canthus of the eye (R2) and eyeball (R3) were estimated. Differences among analysers were analysed by ANOVA (normal distribution) or Friedman test (non-normal distribution), considering thermal images as blocks. Limits of Agreement with the Mean (LOAM) were estimated and agreement plots were drawn. There were no differences (P>0.05) among analysers in temperature (°C, mean±standard deviation or median and 2.5%-97.5% quantiles) estimated in R1 (Tmin=36.3±0.87, Tmax=37.2 (35.9-37.7), Tavg=36.7±0.67), R2 (Tmax=37.2 (36.0-37.8)) and R3 (Tavg=36.1 (34.9-37.3)) for the right eye (n=45), and in R1 (Tmin=36.0 (34.3-37.3), Tmax=36.8±0.49, Tavg=36.4±0.53), R2 (Tmin=35.9±0.71, Tmax=36.7 (35.8-37.7), Tavg=36.3 (35.5-37.5)) and R3 (Tavg=35.9 (34.8-36.6)) for the left eye (n=48); but differences (P<0.05) were present in R3 (Tmin, Tmax) for both eyes and in R2 (Tmin and Tavg) for the right eye. The highest mean differences in temperature (°C) (P<0.05) among analysers were in R3 from the right eye (Tmin=0.70, Tmax=0.25) and the left eye (Tmin=0.64, Tmax=0.10), and in R2 from the right eye (Tmin=0.54, Tavg=0.50). Considering both eyes and all eye regions, the total range for the LOAM (°C) was lower for Tmax (0.42 to 0.93) and Tavg (0.63 to 1.17) than for Tmin (1.49 to 2.70), indicating a higher, medium, and lower agreement among analysers for Tmax, Tavg and Tmin, respectively. Considering the eyes, the total range for the LOAM (°C) was lower in all regions from the left eye (0.42 to 1.59) compared to the right eye (0.79 to 2.70), indicating a higher agreement among analysers from the left eye. Considering the eye regions, the total range for the LOAM (°C) was generally higher in R3 (0.84 to 3.70) than in R1 (0.61 to 1.79) and R2 (0.42 to 2.03), indicating the lowest agreement among analysers in R3 for Tmin (3.70). The present study shows that in sheep, the lacrimal caruncle confers the best agreement among analysers for all estimated temperatures, and that Tmax, across all eye regions, should be regarded as the most reliable estimation.

DrowsiScan: Early Detection of Driver Drowsiness using Deep Learning

In recent years, the rise in car accident fatalities has become a significant global concern, with road security emerging as a critical issue. Among the various factors contributing to road accidents, driver drowsiness stands out as a leading cause. This project aims to address this issue by developing a real-time driver drowsiness detection system using advanced machine vision techniques and deep learning models. Drowsy Driver Detection System has been developed using a non- intrusive machine vision based concepts. The system uses a small snap security camera that points directly towards the driver’s face and monitors the driver eyes in order to descry fatigue. In such a case when fatigue is detected, a warning signal is issued to warn the driver. This report describes how to detect the eyes, and also how to determine if the eyes are open state or close state. The algorithm developed is unique to any presently published papers, which was a primary ideal objective of the project. The system deals with using information attained for the double interpretation of the image to find the edges of the face, which narrows the area of where the eyes located. Once the face is detected, the eyes are found by computing the horizontal averages in the area. Taking into account the knowledge that eye regions in the face present great intensity changes, the eyes are located by chancing the significant intensity changes in the face. Once the eyes are located, measuring the distances between the intensity changes in the eye area determine whether the eyes are in open state or close state.A large distance corresponds to eye check. still, the system draws the conclusion that the driver is falling asleep and issues a warning signal, If the eyes are set up closed for 5 successive frames. The system is also suitable to detect when the eyes can not be set up, and works under reasonable lighting conditions.

Unveiling human eye temperature with deep learning-powered segmentation

Thermal imaging technology presents a promising, non-invasive tool for medical diagnostics, particularly in eye health assessment. However, accurately delineating the eye region in thermal images of the human face remains a significant challenge for temperature analysis. Extracting eye temperature information from ocular thermal images has traditionally been a manual or semi-automated process, making it time-consuming and tedious. Our research aims to address these issues by introducing an automated systematic approach to eye segmentation and temperature analysis. We employ the semantic segmentation deep learning model SegFormer to segment the eye region and a novel method to extract the thermal-ocular temperature within this region of interest (ROI) for analysis of temperature patterns and diagnosis of any abnormalities. The process starts by extracting temperature data from each pixel in the thermal images. Using the mask and these temperature values, only the temperatures within the masked area are isolated for analysis. Validation is done by comparing and calculating the mean square error against the manual method. A self-created dataset of thermal images of human faces is used due to the lack of available datasets for segmentation. SegFormer (B5) achieves a mean intersection over union (MIoU) of 0.9178 and a dice coefficient of 0.9299. The extracted temperature values from the segmented eye regions provide crucial data for assessing eye health and identifying potential abnormalities. This methodical approach contributes to the progression of thermal image analysis for diagnosing eye-related conditions, particularly in ophthalmology, and in medical domains such as fever screening and reliable Computer-Aided Diagnosis Systems (CADS).

Orienting to and away from the eyes in infants at high likelihood for autism when scanning faces.

This study employed eye-tracking technology to investigate the mechanisms underlying reduced gaze towards the eyes in infants at high likelihood (HL) for autism, specifically examining whether it results from avoidance triggered by heightened arousal when looking at the eyes or due to indifference to the eyes (i.e., unwilling to orient to the eyes). Infants at HL for autism and typically developing (TD) infants aged within 24 months were tested. In the experiment, participants' gaze was initially guided to the eye or mouth region immediately before the onset of the face. Latency to orient away from the guided regions, latency to orient to the eyes, and the location of the secondary fixation following the onset of the face were measured. The results showed that: (1) The HL infants looked less at eyes than TD infants; (2) Compared with TD infants, HL infants oriented towards eyes more slowly after being guided to the mouth; (3) After being guided to the eyes, HL infants' secondary fixation fell less in the eye region, and their latency to orient away from the eyes was also tended to be shorter. These results suggest that reduced eye-looking time was presented in HL infants, which was further explained by both eye avoidance and indifference to the eyes. Our study contributes theoretically to understanding the atypical face scanning pattern in autistic people and its related underlying mechanisms. Furthermore, our study provides important insights into the development of early screening tools and intervention protocols for autistic people.

A neuronal marker of eye contact spontaneously activated in neurotypical subjects but not in autistic spectrum disorders

Attention to faces and eye contact are key behaviors for establishing social bonds in humans. In Autism Spectrum Disorders (ASD), a disturbance in neurodevelopment, impaired face processing and gaze avoidance are key clinical features for ASD diagnosis. The biological alterations underlying these impairments are not yet clearly established. Using high-density electroencephalography coupled with multi-variate pattern classification and group blind source separation methods we searched for face- and-face components-related neural signals that could best discriminate visual processing of neurotypical subjects (N = 38) from ASD participants (N = 27). We isolated a face-specific neural signal in the superior temporal sulcus peaking at 240 msec after face-stimulus onset. A machine learning algorithm applied on the extracted neural component reached 74% decoding accuracy at the same latencies, discriminating the neurotypical population from ASD subjects in whom this signal was weak. By manipulating attention on different parts of the face, we also found that the power of the evoked signal in neurotypical subjects varied depending on the region observed: it was strong when the eye region fell on the fovea to decrease on regions further away and outside the stimulus face. Such face and face-components selective neural modulations were not found in ASD, although they did show typical early face-related P100 and N170 signals. These results show that specialized cortical mechanisms for face perception show higher responses for eyes when attention is focused on gaze and that these mechanisms may be particularly affected in autism spectrum disorders.

Frequency-spatial interaction network for gaze estimation

Gaze estimation is a fundamental task in the field of computer vision, which determines the direction a person is looking at. With advancements in Convolutional Neural Networks (CNNs) and the availability of large-scale datasets, appearance-based models have made significant progress. Nonetheless, CNNs exhibit limitations in extracting global information from features, resulting in a constraint on gaze estimation performance. Inspired by the properties of the Fourier transform in signal processing, we propose the Frequency-Spatial Interaction network for Gaze estimation (FSIGaze), which integrates residual modules and Frequency-Spatial Synergistic (FSS) modules. To be specific, its FSS module is a dual-branch structure with a spatial branch and a frequency branch. The frequency branch employs Fast Fourier Transformation to transfer a latent representation to the frequency domain and applies adaptive frequency filter to achieve an image-size receptive field. The spatial branch, on the other hand, can extract local detailed features. Acknowledging the synergistic benefits of global and local information in gaze estimation, we introduce a Dual-domain Interaction Block (DIB) to enhance the capability of the model. Furthermore, we implement a multi-task learning strategy, incorporating eye region detection as an auxiliary task to refine facial features. Extensive experiments demonstrate that our model surpasses other state-of-the-art gaze estimation models on three three-dimensional (3D) datasets and delivers competitive results on two two-dimensional (2D) datasets.

Gaze Zone Classification for Driving Studies Using YOLOv8 Image Classification.

Gaze zone detection involves estimating where drivers look in terms of broad categories (e.g., left mirror, speedometer, rear mirror). We here specifically focus on the automatic annotation of gaze zones in the context of road safety research, where the system can be tuned to specific drivers and driving conditions, so that an easy to use but accurate system may be obtained. We show with an existing dataset of eye region crops (nine gaze zones) and two newly collected datasets (12 and 10 gaze zones) that image classification with YOLOv8, which has a simple command line interface, achieves near-perfect accuracy without any pre-processing of the images, as long as a model is trained on the driver and conditions for which annotation is required (such as whether the drivers wear glasses or sunglasses). We also present two apps to collect the training images and to train and apply the YOLOv8 models. Future research will need to explore how well the method extends to real driving conditions, which may be more variable and more difficult to annotate for ground truth labels.

Assessment of CCMP in Capturing High Winds with Respect to Individual Satellite Datasets

High-wind structures were identified in the Cross-Calibrated Multi-Platform (CCMP) ocean wind vector reanalysis for comparison with winds measured by satellite radiometers, scatterometers, and synthetic aperture radar (SAR) instruments from February to October 2023. The comparison aims to evaluate bias, uncertainty, and spatial correlations with the goal of enhancing the accuracy of ocean wind datasets during tropical cyclones (TCs). In 10° longitude × 10° latitude blocks, each containing a TC, Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) winds are 6.5 and 4.8% higher than CCMP, while Advanced Scatterometer (ASCATB) is 0.8% lower. For extratropical cyclones, AMSR2 and SMAP also show stronger winds with a 5% difference, and ASCATB is about 0.3% weaker compared to CCMP. The comparison between SAR and CCMP for TC winds, sampled at the locations and time frames of SAR tiles, indicates that SAR winds around TCs are about 9% higher than CCMP winds. Using empirically defined TC structural indices, we find that the TCs observed by CCMP are shifted in locations and lack a compact core region. A Random Forest (RF) regressor was applied to TCs in CCMP with corresponding SAR observations, nearly correcting the full magnitude of low bias in CCMP statistically, with a 15 m/s correction in the core region. The hierarchy of importance among the predictors is as follows: CCMP wind speed (62%), distance of SAR pixels to the eye region (21%) and eye center (7%), and distance of CCMP pixels to the eye region (5%) and eye center (5%).

VITILIGO TREATMENT WITH MANIPULATED FORMULATION CONTAINING TOPICAL ANTI-INFLAMMATORY DRUGS: CASE REPORT

Introduction: Vitiligo is a dermatological condition that causes white patches on the skin due to the loss of melanocytes. Classified as an autoimmune disease, it is associated with genetic, environmental factors and oxidative stress. It occurs predominantly in people aged between 10 and 30 years, affecting the self-esteem and quality of life of patients. Objective: This study aimed to monitor a patient using a compounded formulation containing topical anti-inflammatories, regarding improvement in the pigmentation of the spots. Case Report: The volunteer presented vitiligo spots in the eye and eyelid region, leading to self-esteem problems because they are quite visible spots. The formulation containing topical anti-inflammatories was compounded in a Compounding Pharmacy in the city of Maringá (PR) and given to the volunteer, with instructions for use to apply once a day directly on the spots, always at night, after cleaning the affected area. The monitoring was carried out by the students weekly, through photos and application of a questionnaire to the volunteer. The treatment was carried out for 30 days, without any complications, discomfort or skin irritation. The patient reported having undergone the treatment without interruption, and after the total treatment time, there was a significant improvement in pigmentation. In the questionnaire applied, the volunteer reported that there was an improvement in the appearance of the spots, and the result can be seen in the weekly photos. This research was approved by the Ethics Committee for Research involving Human Beings of Uningá (CEP/Uningá), under protocol number 6,950,683.

Gaze behavior in response to affect during natural social interactions.

Attention in social interactions is directed by social cues such as the face or eye region of an interaction partner. Several factors that influence these attentional biases have been identified in the past. However, most findings are based on paradigms with static stimuli and no interaction potential. Therefore, the current study investigated the influence of one of these factors, namely facial affect in natural social interactions using an evaluated eye-tracking setup. In a sample of 35 female participants, we examined how individuals' gaze behavior responds to changes in the facial affect of an interaction partner trained in affect modulation. Our goal was to analyze the effects on attention to facial features and to investigate their temporal dynamics in a natural social interaction. The study results, obtained from both aggregated and dynamic analyses, indicate that facial affect has only subtle influences on gaze behavior during social interactions. In a sample with high measurement precision, these findings highlight the difficulties of capturing the subtleties of social attention in more naturalistic settings. The methodology used in this study serves as a foundation for future research on social attention differences in more ecologically valid scenarios.

Glial Cell Responses and Gene Expression Dynamics in Retinas of Treated and Untreated RPE65 Mutant Dogs.

The long-term evaluation of RPE65 gene augmentation initiated in middle-aged RPE65 mutant dogs previously uncovered notable inter-animal and intra-retinal variations in treatment efficacy. The study aims to gain deeper insights into the status of mutant retinas and assess the treatment impact. Immunohistochemistry utilizing cell-specific markers and reverse transcription-quantitative PCR (RT-qPCR) analysis were conducted on archival retinal sections from normal and RPE65 mutant dogs. Untreated middle-aged mutant retinas exhibited marked downregulation in the majority of 20 examined genes associated with key retinal pathways. These changes were accompanied by a moderate increase in microglia numbers, altered expression patterns of glial-neuronal transmitter recycling proteins, and gliotic responses in Müller glia. Analysis of advanced-aged mutant dogs revealed mild outer nuclear layer loss in the treated eye compared to moderate loss in the corresponding retinal regions of the untreated control eye. However, persistent Müller glial stress response along with photoreceptor synapse loss were evident in both treated and untreated eyes. Photoreceptor synaptic remodeling, infrequent in treated regions, was observed in all untreated advanced-aged retinas, accompanied by a progressive increase in microglial cells indicative of ongoing inflammation. Interestingly, about half of the examined genes showed similar expression levels between treated and untreated advanced-aged mutant retinas, with some reaching normal levels. Gene expression data suggest a shift from pro-degenerative mechanisms in middle-aged mutant retinas to more compensatory mechanisms in preserved retinal regions at advanced stages, despite ongoing degeneration. Such shift, potentially attributed to a number of surviving resilient cells, may influence disease patterns and treatment outcomes.

Hierarchical Event-RGB Interaction Network for single-eye expression recognition

The Single-eye Expression Recognition task stands as a crucial vision task, aimed at decoding human emotional states through careful examination of the eye region. Nevertheless, traditional cameras face challenges in detecting and capturing relevant biological information, especially under demanding lighting conditions such as dim environments, high exposure scenarios, or when other radiation sources are present. In this regard, we use a new type of sensor data that can resist extreme lighting conditions, namely event camera data, to improve the performance of single-eye expression recognition. To this end, we propose a novel Hierarchical Event-RGB Interaction Network (HI-Net), to fully integrate RGB and event data to overcome the extreme lighting challenges faced by the single-eye expression recognition task. The HI-Net contains two novel designs: Event-RGB Semantic Interaction Mechanism (ER-SIM) and Hierarchical Semantics Modeling (HSM) Scheme. The former aims to achieve interaction between Event and RGB modality semantics, while the latter aims to obtain high-quality modality semantic representations. In the ER-SIM, we employ an effective cross-attention mechanism to facilitate information fusion, to adaptively integrate and complement multi-scale Event and RGB semantics to cope with extreme lighting conditions. In HSM Scheme, we first explore multi-scale contextual semantics for the event modality and the RGB modality respectively. Then, we perform a semantics interaction strategy for these multi-scale contextual semantics, to enhance each modality's semantic representation. Extensive experiments demonstrate that our HI-Net significantly outperforms many state-of-the-art methods on the single-eye expression recognition task, especially under degraded lighting conditions.

Нарушение подвижности глазного яблока у пациентов с травматическим косоглазием

Relevance. Limitation of eyeball mobility is one of the main signs of traumatic strabismus. The following main factors of its occurrence have been identified: trauma to the eye and periorbital region, orbital fracture, concussion and severe cranio-orbital or traumatic brain injury. Oculomotor function may suffer due to paresis/paralysis of the extraocular muscle, its mechanical damage or entrapment in the area of the fracture of the orbital bones (in cases where strabismus has developed as a result of an orbital fracture), or due to the formation of severe tissue fibrosis and restriction. Purpose. To study the dynamics of oculomotor function before and after surgical treatment of traumatic strabismus in patients with dystopia of the eyeball and without it. Material and methods. A retrospective analysis of outpatient records of 269 patients with traumatic strabismus was carried out. Results. Dystopia of the eyeball was detected in 34%. Exophthalmos accounts for approximately a third of all dystopias occurring in traumatic strabismus. An isolated fracture of the orbital wall did not cause exophthalmos. In such cases, other types of dystopias were noted. But in more extensive and severe injuries to the skull, exophthalmos occurred in 10% of cases. Among all cases of exophthalmos, in 40% it developed as a result of injury to the eye or periorbital region, in 60% – with severe cranio-orbital damages (COD) and head injury. It was found that as a result of surgical treatment, the number of patients with normal eyeball mobility increased from 16 to 24%. Lack of eyeball mobility was noted in 7% of patients, while before treatment their number was almost 19%. In the group without dystopia with moderate oculomotor disorders, after treatment, the mobility of the eyeball did not improve in 65% of patients; it improved in 31% of patients; in the group with dystopia, 41% retained the same degree of mobility limitation, and improvement (decrease in the degree of limitation) was detected in 55% of patients. Conclusion.It has been established that dystopia causes more severe oculomotor disorders. However, adequate surgical treatment can improve eye mobility in more than half of the patients. Key words: traumatic strabismus, dystopia, orbital rupture, eyeball mobility, restriction, fibrosis

Enhancement of eye socket recognition performance using inverse histogram fusion images and the Gabor transform

AbstractThe eye socket is a cavity in the skull that encloses the eyeball and its surrounding muscles. It has unique shapes in individuals. This study proposes a new recognition method that relies on the eye socket shape and region. This method involves the utilization of an inverse histogram fusion image to generate Gabor features from the identified eye socket regions. These Gabor features are subsequently transformed into Gabor images and employed for recognition by utilizing both traditional methods and deep‐learning models. Four distinct benchmark datasets (Flickr30, BioID, Masked AT &amp; T, and CK+) were used to evaluate the method's performance. These datasets encompass a range of perspectives, including variations in eye shape, covering, and angles. Experimental results and comparative studies indicate that the proposed method achieved a significantly () higher accuracy (average value greater than 92.18%) than that of the relevant identity recognition method and state‐of‐the‐art deep networks (average value less than 78%). We conclude that this improved generalization has significant implications for advancing the methodologies employed for identity recognition.

Intentional looks facilitate faster responding in observers

Humans construct rich representations of other people’s mental states. Here we investigated how intentionality in eye gaze affected perception and responses to gaze. Observers viewed videos of human gazers looking left or right. Unbeknownst to the observers, the gazers could either choose where to look (self-chosen gaze) or were explicitly instructed where to look (computer-instructed gaze). In Experiment 1, observers reported the direction of the gazer’s upcoming look before the eye movement was initiated. Faster responses were found for self-chosen relative to computer-instructed gaze. In Experiments 2 and 3, observers responded by reporting the location of a peripheral target that appeared at the gazed-at or not gazed-at location. Faster responses were found for gazed-at relative to not gazed-at targets and at longer cue-target intervals for self-chosen relative to computer-instructed gaze. The examination of the eye movement kinematics indicated that self-chosen gaze shifts were marked by a larger magnitude of motion within the eye region prior to the eye movement occurring relative to computer-instructed ones. Thus, perceived intentionality in eye gaze facilitates responses in observers with the information about mental states communicated via subtle properties of eye motion.

A Real-Time Embedded System for Driver Drowsiness Detection Based on Visual Analysis of the Eyes and Mouth Using Convolutional Neural Network and Mouth Aspect Ratio.

Currently, the number of vehicles in circulation continues to increase steadily, leading to a parallel increase in vehicular accidents. Among the many causes of these accidents, human factors such as driver drowsiness play a fundamental role. In this context, one solution to address the challenge of drowsiness detection is to anticipate drowsiness by alerting drivers in a timely and effective manner. Thus, this paper presents a Convolutional Neural Network (CNN)-based approach for drowsiness detection by analyzing the eye region and Mouth Aspect Ratio (MAR) for yawning detection. As part of this approach, endpoint delineation is optimized for extraction of the region of interest (ROI) around the eyes. An NVIDIA Jetson Nano-based device and near-infrared (NIR) camera are used for real-time applications. A Driver Drowsiness Artificial Intelligence (DD-AI) architecture is proposed for the eye state detection procedure. In a performance analysis, the results of the proposed approach were compared with architectures based on InceptionV3, VGG16, and ResNet50V2. Night-Time Yawning-Microsleep-Eyeblink-Driver Distraction (NITYMED) was used for training, validation, and testing of the architectures. The proposed DD-AI network achieved an accuracy of 99.88% with the NITYMED test data, proving superior to the other networks. In the hardware implementation, tests were conducted in a real environment, resulting in 96.55% and 14 fps on average for the DD-AI network, thereby confirming its superior performance.

An investigation on the face inversion effect in deaf children.

The face inversion effect is an important indicator of holistic face perception and reflects the developmental level of face processing. This study examined the face inversion effect in deaf or hard of hearing (DHH) children aged 7-17 using the face dimensions task. This task uses photographic images of a face, in which configural and featural information in the eye and mouth regions have been parametrically and independently manipulated. The study aimed to discuss the effect of face inversion on facial processing in DHH children, including two aspects of information processing types (configural versus featural) and processing regions (eyes versus mouth) and compared the results with hearing children. The results revealed that DHH children aged 7-17years exhibit significant face inversion effect, with disruptions observed in both the featural and configural processing of eyes and mouths when faces were inverted. Configural processing was more affected by inversion than featural processing in all children, with larger differences observed in DHH children than in hearing children. This supports the dual-mode hypothesis of holistic face processing. Age correlations were observed in the sensitivity of DHH children to face inversion effect but not among hearing children. The inversion effect of configural mouth processing decreases with age in DHH children.