![]() The typical desktop eye tracker’s accuracy is between 1 and 2 degrees, and the best commercial eye tracker’s accuracy is about 0.5 degrees. Īt present, there are many commercial eye trackers available. Nowadays, due to immense improvements in computing speed, low-cost hardware, and digital video processing, gaze tracking tools are becoming available for new applications, such as virtual reality, web advertisements, and gaming. ![]() Real-time eye-tracking was possible back in the 1980s however, at that time, eye-tracking applications were limited to cognitive and psychological processes and medical research. ![]() Since a person’s gaze is an observable indicator of visual attention, eye movement research started back in the early 19th century. Gaze estimation is a significant research problem due to its various applications in real life, including human–computer interactions, behavioral analysis, virtual reality, and health care. 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. 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. Then, we tested different combinations of CNN parameters, including the learning and dropout rates. First, we used the web camera to collect a dataset of face and eye images. 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. ![]() 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. This paper uses a convolutional neural network (CNN) for person-specific gaze estimation. 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. 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. In the circa-£250 space, do you prefer a cheap and cheerful GTX 1660 Ti, or a top-drawer GTX 1660 Super? It is a case of personal preference, and though a no-frills alternative will deliver similar performance, we view the ultra-low noise levels of the Gaming X as a star attraction.īottom line: it could do with being £10 cheaper, but if you want a high-quality GTX 1660 Super, the MSI Gaming X delivers.Gaze estimation is an established research problem in computer vision. Performance is excellent at 1080p with maxed-out quality settings, but the congested nature of Nvidia's product stack leaves would-be buyers facing a bit of a dilemma. ![]() Offering solid build quality, eye-catching aesthetics and a decent factory overclock, the card is more than a match for most competitors in its class and sets itself apart through a cooler that is silent when idle and barely audible under load. MSI has joined the GTX 1660 Super party with half-a-dozen models that repurpose existing cooler designs to fit Nvidia's latest mid-range Turing GPU.Īt the top of the pile sits the £250 Gaming X. though a no-frills alternative will deliver similar performance, we view the ultra-low noise levels of the Gaming X as a star attraction. ![]()
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