KM
K. Mirinski
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Eye tracking is a key enabling technology for wearable and extended-reality devices, but conventional frame-based systems struggle to capture the eye's rapid motion within the tight compute and power budgets of such hardware. Event cameras, which report per-pixel brightness changes asynchronously at microsecond resolution, are a natural fit for this setting, yet most event-based methods rely on heavy neural networks that are impractical to deploy on resource-constrained devices. This thesis presents a lightweight, training-free pipeline for near-eye gaze estimation that maps the tracked pupil to a point of gaze on the screen. The pupil is first detected in gray-scale frames using a purely geometric procedure of thresholding, morphological filtering, and ellipse fitting, and its center is then propagated between frames directly on the event stream by a points-to-edge template tracker, providing high-frequency updates without reconstructing an image. The pupil observation is mapped to gaze using a polynomial regressor. Evaluated on two near-eye datasets, the geometric detector matches a model-based baseline and approaches a supervised segmentation network, while the full system runs end-to-end in under a millisecond on a CPU - several orders of magnitude cheaper than learned alternatives. The work trades some gaze accuracy for the ability to run without a GPU or training data, at the cost of a small number of per-subject detection thresholds, offering a practical path toward efficient, deployable event-based eye tracking.
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Eye tracking is a key enabling technology for wearable and extended-reality devices, but conventional frame-based systems struggle to capture the eye's rapid motion within the tight compute and power budgets of such hardware. Event cameras, which report per-pixel brightness changes asynchronously at microsecond resolution, are a natural fit for this setting, yet most event-based methods rely on heavy neural networks that are impractical to deploy on resource-constrained devices. This thesis presents a lightweight, training-free pipeline for near-eye gaze estimation that maps the tracked pupil to a point of gaze on the screen. The pupil is first detected in gray-scale frames using a purely geometric procedure of thresholding, morphological filtering, and ellipse fitting, and its center is then propagated between frames directly on the event stream by a points-to-edge template tracker, providing high-frequency updates without reconstructing an image. The pupil observation is mapped to gaze using a polynomial regressor. Evaluated on two near-eye datasets, the geometric detector matches a model-based baseline and approaches a supervised segmentation network, while the full system runs end-to-end in under a millisecond on a CPU - several orders of magnitude cheaper than learned alternatives. The work trades some gaze accuracy for the ability to run without a GPU or training data, at the cost of a small number of per-subject detection thresholds, offering a practical path toward efficient, deployable event-based eye tracking.
Imperceptible Backdoor Attacks for Deep Regression Models
Adapting the SIG Backdoor Attack to the Head Pose Estimation Task
With the rise of deep learning and the widespread use of deep neural networks, backdoor attacks have become a significant security threat, drawing considerable research interest. One such attack is the SIG backdoor attack, which introduces signals to the images. We look into three types of SIG backdoor attacks - ramp, triangle, and sinusoidal signals. Most of the works in the field of AI security, however, have focused on deep classification tasks, leaving deep regression tasks unexplored. In this study, we adapt the SIG backdoor attack for use in a deep regression model (DRM) used to estimate head pose. Our objective is to create a backdoor attack that remains imperceptible to the human eye while being detectable by the DRM. To evaluate the effectiveness of our attack, we employ two approaches: average angular error and accuracy in a discretized continuous space. Additionally, we adapt fine-tuning as a countermeasure against the backdoor attack. By implementing this strategy, we aim to reduce the risk of backdoor attacks and improve the robustness of deep regression models in head pose estimation.
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With the rise of deep learning and the widespread use of deep neural networks, backdoor attacks have become a significant security threat, drawing considerable research interest. One such attack is the SIG backdoor attack, which introduces signals to the images. We look into three types of SIG backdoor attacks - ramp, triangle, and sinusoidal signals. Most of the works in the field of AI security, however, have focused on deep classification tasks, leaving deep regression tasks unexplored. In this study, we adapt the SIG backdoor attack for use in a deep regression model (DRM) used to estimate head pose. Our objective is to create a backdoor attack that remains imperceptible to the human eye while being detectable by the DRM. To evaluate the effectiveness of our attack, we employ two approaches: average angular error and accuracy in a discretized continuous space. Additionally, we adapt fine-tuning as a countermeasure against the backdoor attack. By implementing this strategy, we aim to reduce the risk of backdoor attacks and improve the robustness of deep regression models in head pose estimation.