Search Results (16)

This pilot study examines how experimentally induced emotional states interact with the visual properties of urban environments to shape comfort perception. A controlled laboratory experiment was conducted with 17 participants assigned to one of four emotional conditions (Fear, Anger, Sad, Happy) through audio-visual induction. Participants evaluated 73 building façade and 42 pedestrian streetscape stimuli from three urban areas in Kitakyushu, Japan (Wakamatsu, Tobata, Mojiko) using a multi-method framework combining fractal analysis (D, Λ), six pedestrian visual metrics, webcam-based eye-tracking (Visual Attention Score, VAS), and self-reported comfort votes. Emotion induction was effective for Fear and Anger groups and partial for Sad and Happy groups, with the latter attributable to experimental fatigue. Cross-method correlation analysis revealed that fractal dimension D significantly predicted comfort vote consensus (Spearman r = 0.369, p = 0.013), while VAS showed no significant relationship with comfort votes (r = 0.097, ns) or with fractal dimension (r = 0.015, ns), confirming that visual attention and comfort preference are independent dimensions. For building façades, the ‘Complex but Organized’ fractal profile (D ≥ 1.70, Λ < 0.60) was the consistent comfort driver across all emotion groups. For pedestrian streetscapes, low spatial enclosure and spatially integrated tree canopy were the primary comfort predictors. Multi-method synthesis identified five empirical paradoxes and three design principles: (1) target D ≥ 1.70 with Λ < 0.60; (2) prioritize spatially integrated canopy over visible greenery quantity; and (3) leverage civic legibility as an independent comfort pathway. These findings support the development of emotion-independent frameworks for urban comfort evaluation. Replication with larger, more diverse samples is recommended. Full article

Eye movement classification, particularly the identification of fixations and saccades, plays a vital role in advancing our understanding of neurological functions and cognitive processing. Conventional modalities of data, such as RGB webcams, often face limitations such as motion blur, latency and susceptibility to noise. Neuromorphic Vision Sensors, also known as event cameras (ECs), capture pixel-level changes asynchronously and at a high temporal resolution, making them well suited for detecting the swift transitions inherent to eye movements. However, the resulting data are sparse, which makes them less well suited for use with conventional algorithms. Spiking Neural Networks (SNNs) are gaining attention due to their discrete spatio-temporal spike mechanism ideally suited for sparse data. These networks offer a biologically inspired computational paradigm capable of modeling the temporal dynamics captured by event cameras. This study validates the use of Spiking Neural Networks (SNNs) with event cameras for efficient eye movement classification. We manually annotated the EV-Eye dataset, the largest publicly available event-based eye-tracking benchmark, into sequences of saccades and fixations, and we propose a convolutional SNN architecture operating directly on spike streams. Our model achieves an accuracy of 94% and a precision of 0.92 across annotated data from 10 users. As the first work to apply SNNs to eye movement classification using event data, we benchmark our approach against spiking baselines such as SpikingVGG and SpikingDenseNet, and additionally provide a detailed computational complexity comparison between SNN and ANN counterparts. Our results highlight the efficiency and robustness of SNNs for event-based vision tasks, with over one order of magnitude improvement in computational efficiency, with implications for fast and low-power neurocognitive diagnostic systems. Full article

This paper presents the design and feasibility evaluation of a low-cost, head-mounted pupil dilation tracking system based on computer vision. The proposed solution employs a standard webcam and active infrared illumination, enabling stable eye image acquisition under controlled lighting conditions. The developed image processing pipeline incorporates adaptive contrast enhancement and geometric pupil detection, allowing for the estimation of relative changes in pupil diameter in real time. System evaluation was conducted in a controlled experiment involving 24 participants performing an N-back task with emotional modulation, a well-established paradigm for eliciting task-evoked pupillary responses under constant working-memory demands. The results revealed statistically significant changes in relative pupil dilation in response to stimuli with varying emotional valence during a working memory task, confirming the system’s ability to capture task-evoked pupillary responses (TEPRs). The proposed system constitutes a low-cost research tool for studies of task engagement and physiological responses in the context of human–computer interaction and psychophysiology, with a focus on the analysis of functional pupilometric changes. Full article

Eye-tracking for user experience analysis has traditionally relied on dedicated hardware, which is often costly and imposes restrictive operating conditions. As an alternative, solutions utilizing ordinary webcams have attracted significant interest due to their affordability and ease of use. However, a major limitation persists in these vision-based methods: sensitivity to head movements. Therefore, users are often required to maintain a rigid head position, leading to discomfort and potentially skewed results. To address this challenge, this paper proposes a robust eye-tracking methodology designed to accommodate head motion. Our core technique involves mapping the displacement of the pupil center from a dynamically updated reference point to estimate the gaze point. When head movement is detected, the system recalculates the head-pointing coordinate using estimated head pose and user-to-screen distance. This new head position and the corresponding pupil center are then established as the fresh benchmark for subsequent gaze point estimation, creating a continuous and adaptive correction loop. We conducted accuracy tests with 22 participants. The results demonstrate that our method surpasses the performance of many current methods, achieving mean gaze errors of 1.13 and 1.37 degrees in two testing modes. Further validation in a smooth pursuit task confirmed its efficacy in dynamic scenarios. Finally, we applied the method in a real-world gaming context, successfully extracting fixation counts and gaze heatmaps to analyze visual behavior and UX across different game modes, thereby verifying its practical utility. Full article

Eye-tracking technology enables communication for individuals with muscle control difficulties, making it a valuable assistive tool. Traditional systems rely on electrooculography (EOG) or infrared devices, which are accurate but costly and invasive. While vision-based systems offer a more accessible alternative, they have not been extensively explored for eye-writing recognition. Additionally, the natural instability of eye movements and variations in writing styles result in inconsistent signal lengths, which reduces recognition accuracy and limits the practical use of eye-writing systems. To address these challenges, we propose a novel vision-based eye-writing recognition approach that utilizes a webcam-captured dataset. A key contribution of our approach is the introduction of a Discrete Fourier Transform (DFT)-based length normalization method that standardizes the length of each eye-writing sample while preserving essential spectral characteristics. This ensures uniformity in input lengths and improves both efficiency and robustness. Moreover, we integrate a hybrid deep learning model that combines 1D Convolutional Neural Networks (CNN) and Temporal Convolutional Networks (TCN) to jointly capture spatial and temporal features of eye-writing. To further improve model robustness, we incorporate data augmentation and initial-point normalization techniques. The proposed system was evaluated using our new webcam-captured Arabic numbers dataset and two existing benchmark datasets, with leave-one-subject-out (LOSO) cross-validation. The model achieved accuracies of 97.68% on the new dataset, 94.48% on the Japanese Katakana dataset, and 98.70% on the EOG-captured Arabic numbers dataset—outperforming existing systems. This work provides an efficient eye-writing recognition system, featuring robust preprocessing techniques, a hybrid deep learning model, and a new webcam-captured dataset. Full article

This study investigated how simulated ophthalmic visual field deficits affect visual attention and economic information processing. Using webcam-based eye tracking, 227 participants with normal vision recruited through Amazon Mechanical Turk were assigned to control, central vision loss, peripheral vision loss, or scattered vision loss simulation conditions. Participants viewed economic stimuli of varying complexity while eye movements, cognitive load, and comprehension were measured. All deficit conditions showed altered oculomotor behaviors. Central vision loss produced the most severe impairments: 43.6% increased fixation durations, 68% longer scanpaths, and comprehension accuracy of 61.2% versus 87.3% for controls. Visual deficits interacted with information complexity, showing accelerated impairment for complex stimuli. Mediation analysis revealed 47% of comprehension deficits were mediated through altered attention patterns. Cognitive load was significantly elevated, with central vision loss participants reporting 84% higher mental demand than controls. These findings demonstrate that visual field deficits fundamentally alter economic information processing through both direct perceptual limitations and compensatory attention strategies. Results demonstrate the feasibility of webcam-based eye tracking for studying simulated visual deficits and suggest that different types of simulated visual deficits may require distinct information presentation strategies. Full article

Gaze tracking and estimation are essential for understanding human behavior and enhancing human–computer interactions. This study introduces an innovative, cost-effective solution for real-time gaze tracking using a standard webcam, providing a practical alternative to conventional methods that rely on expensive infrared (IR) cameras. Traditional approaches, such as Pupil Center Corneal Reflection (PCCR), require IR cameras to capture corneal reflections and iris glints, demanding high-resolution images and controlled environments. In contrast, the proposed method utilizes a convolutional neural network (CNN) trained on webcam-captured images to achieve precise gaze estimation. The developed deep learning model achieves a mean squared error (MSE) of 0.0112 and an accuracy of 90.98% through a novel trajectory-based accuracy evaluation system. This system involves an animation of a ball moving across the screen, with the user’s gaze following the ball’s motion. Accuracy is determined by calculating the proportion of gaze points falling within a predefined threshold based on the ball’s radius, ensuring a comprehensive evaluation of the system’s performance across all screen regions. Data collection is both simplified and effective, capturing images of the user’s right eye while they focus on the screen. Additionally, the system includes advanced gaze analysis tools, such as heat maps, gaze fixation tracking, and blink rate monitoring, which are all integrated into an intuitive user interface. The robustness of this approach is further enhanced by incorporating Google’s Mediapipe model for facial landmark detection, improving accuracy and reliability. The evaluation results demonstrate that the proposed method delivers high-accuracy gaze prediction without the need for expensive equipment, making it a practical and accessible solution for diverse applications in human–computer interactions and behavioral research. Full article

This study investigates the impact of intelligible background speech on reading disruption utilising neuromarketing methodologies, specifically an eye-tracking webcam (Tobii Sticky) and AI eye-tracking software (Predict, v.1.0.). A cohort of 144 participants from Oxford Business College underwent emotional impact testing, while an AI eye-tracking algorithm analysed attention patterns across 180,000 eye-tracking recordings. Two articles from OxConnect Magazine were presented in varying background formats. Python-based analysis revealed that the HND article consistently outperformed OxFoodbank in maintaining reader engagement and attention. The HND’s structured content yielded higher total attention (white: 49.43%, black: 48.19%) and end attention (white: 27.58%, black: 28.43%). Emotion analysis indicated that HND elicited a more neutral (white mean difference: 0.1514, black: 0.1008) and consistent emotional response, with reduced puzzlement (white mean difference: −0.3296, black: −0.0918). Furthermore, this demonstrates the effectiveness of integrating AI eye-tracking algorithms with webcam eye trackers for comprehensive reading behaviour analysis. These findings provide valuable insights for colleges developing e-magazines, offering evidence-based strategies to enhance student engagement and information retention. By implementing well-structured, visually appealing content, educational institutions can optimise their digital publications to maintain reader attention even in the presence of background distractions, ultimately improving the effectiveness of their e-magazines as educational tools. Full article

Virtual speeches are a very popular way for remote multi-user communication, but it has the disadvantage of the lack of eye contact. This paper proposes the evaluation of an online audience attention based on gaze tracking. Our research only uses webcams to capture the audience’s head posture, gaze time, and other features, providing a low-cost method for attention monitoring with reference values across multiple domains. Meantime, we also propose a set of indexes which can be used to evaluate the audience’s degree of attention, making up for the fact that the speaker cannot gauge the audience’s concentration through eye contact during online speeches. We selected 96 students for a 20 min group simulation session and used Spearman’s correlation coefficient to analyze the correlation between our evaluation indicators and concentration. The result showed that each evaluation index has a significant correlation with the degree of attention (p = 0.01), and all the students in the focused group met the thresholds set by each of our evaluation indicators, while the students in the non-focused group failed to reach the standard. During the simulation, eye movement data and EEG signals were measured synchronously for the second group of students. The EEG results of the students were consistent with the systematic evaluation. The performance of the measured EEG signals confirmed the accuracy of the systematic evaluation. Full article

Selecting the gender of a celebrity for fast-moving consumer goods (FMCG) advertising presents a strategic challenge. Previous research has predominantly concentrated on comparing celebrity spokespersons with non-celebrities, frequently neglecting the intricate distinctions in the effectiveness of male versus female endorsers. This study addresses this research gap by employing both traditional and neuromarketing methodologies. By integrating eye-tracking technology via RealEye and questionnaire-based surveys, the results indicate that female celebrities are more effective in capturing visual attention, whereas male celebrities are more effective in enhancing perceived trustworthiness. These findings are pivotal for both academic research and commercial strategy, as they elucidate the optimal selection of celebrity gender for maximizing FMCG advertising efficacy. Full article

This paper presents a novel approach to mind-wandering prediction in the context of webcam-based online learning. We implemented a Singular Value Decomposition (SVD)-based 1D temporal eye-signal extraction method, which relies solely on eye landmark detection and eliminates the need for gaze tracking or specialized hardware, then extract suitable features from the signals to train the prediction model. Our thorough experimental framework facilitates the evaluation of our approach alongside baseline models, particularly in the analysis of temporal eye signals and the prediction of attentional states. Notably, our SVD-based signal captures both subtle and major eye movements, including changes in the eye boundary and pupil, surpassing the limited capabilities of eye aspect ratio (EAR)-based signals. Our proposed model exhibits a 2% improvement in the overall Area Under the Receiver Operating Characteristics curve (AUROC) metric and 7% in the F1-score metric for ‘not-focus’ prediction, compared to the combination of EAR-based and computationally intensive gaze-based models used in the baseline study These contributions have potential implications for enhancing the field of attentional state prediction in online learning, offering a practical and effective solution to benefit educational experiences. Full article

(1) Background: Our previous research provides evidence that vergence eye movements may significantly influence cognitive processing and could serve as a reliable measure of cognitive issues. The rise of consumer-grade eye tracking technology, which uses sophisticated imaging techniques in the visible light spectrum to determine gaze position, is noteworthy. In our study, we explored the feasibility of using webcam-based eye tracking to monitor the vergence eye movements of patients with Mild Cognitive Impairment (MCI) during a visual oddball paradigm. (2) Methods: We simultaneously recorded eye positions using a remote infrared-based pupil eye tracker. (3) Results: Both tracking methods effectively captured vergence eye movements and demonstrated robust cognitive vergence responses, where participants exhibited larger vergence eye movement amplitudes in response to targets versus distractors. (4) Conclusions: In summary, the use of a consumer-grade webcam to record cognitive vergence shows potential. This method could lay the groundwork for future research aimed at creating an affordable screening tool for mental health care. Full article

The increasing popularity of neuromarketing has led to the emergence of various measurement methods, such as webcam-based eye-tracking technology. Webcam-based eye-tracking technology is noteworthy not only for its use in laboratories but also for its ability to be applied to participants online in their natural environments through a link. However, the complexity of e-commerce interfaces necessitates high performance in eye-tracking methods. This complexity and the applicability of webcam-based eye-tracking technology in various environments have raised research questions about how its performance changes depending on the type and location of lighting. To answer these questions, experiments were conducted with 30 users in two different experimental environments illuminated by artificial and natural methods, with the lighting from the left, right, and front. Participants were asked to focus on targets located in specially prepared graphics for the experiment. In the heatmaps obtained in the eye-tracking tests, the distance and angular difference between the focal point and the target point were measured using the polar coordinate system. The findings indicate that measurements taken with lighting coming from the center were more efficient in both natural and artificial lighting types and measurements taken under natural lighting performed 24% better than artificial ones. Web camera-based eye-tracking technology is a promising method. However, detailed statistical analyses have demonstrated that for complex interfaces like e-commerce, the position and type of lighting are crucial parameters. Full article

Gaze estimation is an established research problem in computer vision. It has various applications in real life, from human–computer interactions to health care and virtual reality, making it more viable for the research community. Due to the significant success of deep learning techniques in other computer vision tasks—for example, image classification, object detection, object segmentation, and object tracking—deep learning-based gaze estimation has also received more attention in recent years. This paper uses a convolutional neural network (CNN) for person-specific gaze estimation. The person-specific gaze estimation utilizes a single model trained for one individual user, contrary to the commonly-used generalized models trained on multiple people’s data. We utilized only low-quality images directly collected from a standard desktop webcam, so our method can be applied to any computer system equipped with such a camera without additional hardware requirements. First, we used the web camera to collect a dataset of face and eye images. Then, we tested different combinations of CNN parameters, including the learning and dropout rates. Our findings show that building a person-specific eye-tracking model produces better results with a selection of good hyperparameters when compared to universal models that are trained on multiple users’ data. In particular, we achieved the best results for the left eye with 38.20 MAE (Mean Absolute Error) in pixels, the right eye with 36.01 MAE, both eyes combined with 51.18 MAE, and the whole face with 30.09 MAE, which is equivalent to approximately 1.45 degrees for the left eye, 1.37 degrees for the right eye, 1.98 degrees for both eyes combined, and 1.14 degrees for full-face images. Full article

Remote eye tracking technology has suffered an increasing growth in recent years due to its applicability in many research areas. In this paper, a video-oculography method based on convolutional neural networks (CNNs) for pupil center detection over webcam images is proposed. As the first contribution of this work and in order to train the model, a pupil center manual labeling procedure of a facial landmark dataset has been performed. The model has been tested over both real and synthetic databases and outperforms state-of-the-art methods, achieving pupil center estimation errors below the size of a constricted pupil in more than 95% of the images, while reducing computing time by a 8 factor. Results show the importance of use high quality training data and well-known architectures to achieve an outstanding performance. Full article