使用CNN进行眼动分类

Milu Prince, N. Santhosh, Nimitha Thankachan, Reshma Sudarsan, V. Anjusree
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引用次数: 3

摘要

在当今世界,眼睛在技术进步中具有重要意义。目光接触和凝视是沟通的重要因素。眼动在各种技术中有着广泛的应用。近年来,眼动检测受到了广泛的关注。我们的目标是创建一种使用深度卷积神经网络的方法,该方法将并行运行两只眼睛进行视觉特征提取。用于训练和验证的数据集图像来自眼嵌合体数据集。测试是通过输入实时视频完成的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Eye Movement Classification Using CNN
In today’s world eye has significant importance in technological advancements. Eye contact and eye gaze are an important factor for communication. Eye movement has a wide range of applications in various technology. In recent years, eye movement detection is gaining a lot of attention. Our aim is to create an approach that employs deep convolutional neural networks which will run for both eyes in parallel for visual feature extractions. Dataset images for training and validation were obtained from the eye chimera dataset. Testing is done by inputting real-time videos.
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