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Title: Deep learning algorithms and applications
Authors: Ong, Yu Fei
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2019
Abstract: This paper presents an appearance-based gaze-tracking implementation called Browser Eye Tracker (BET). BET is a convolutional neural network for real-time (>30fps) eye- tracking that can run on any device with a web browser without first downloading anything or buying specialised eye-tracking webcams. BET achieves a prediction error 2% lower than previous in-browser approaches on average. An in-browser Auto Sampler (AS) for automated sample collection, a Gaze-tracking Playground (GP) for comparing different models and Real-Time Prediction Testing (RTPT) were also implemented as part of the project.
Schools: School of Electrical and Electronic Engineering 
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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