Adversarial Attack and Training on Deep Learning-based Gaze estimation

Recently, while gaze estimation has gained a substantial improvement by using deep learning models, research had shown that neural networks are weak against adversarial attacks. Despite researchers has been done numerous on adversarial training, there are little to no studies on adversarial training in gaze estimation. Therefore, the objective...

Exploring the Influence of Facial Features Beyond the Eyes on Gaze Estimation

Gaze estimation holds significant importance in various applications. Pioneering research has demonstrated state-of-the-art performance in gaze estimation models by utilizing deep Convolutional Neural Networks (CNNs) and incorporating full facial images as input, instead of or in addition to solely using one or both eye images. Facial images...

Channel Selection for Faster Deep Learning-based Gaze Estimation in the Frequency Domain: A frequency domain approach to reducing latency in deep learning gaze estimation

Gaze estimation is an important area of research used in a wide range of applications. However, existing models trained for gaze estimation often suffer from high computational costs. In this study, frequency domain channel selection techniques were explored to decrease these costs by reducing the size of the input data. The main research...

Practical Approaches towards Complete Real-time Gaze Tracking

Visual context plays a key role in many computer vision tasks, and performance of eye/gaze-tracking methods also benefit from it. However, the size of contextual information (e.g. full face image) is very large w.r.t the primary input i.e. cropped image of the eye. This adds large computational costs to the algorithm and makes it inefficient,...