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iLieDown - Improved Display Orientation For Handheld Devices Using Convolutional Neural Networks.pdf

Description
91% of smartphone and tablet users experience a problem with their device screen being oriented the wrong way during use [11]. In [11], the authors proposed iRotate, a previous solution which uses computer vision to solve the orientation problem. We propose iLieDown, an improved method of automatically rotating smartphones, tablets,…

91% of smartphone and tablet users experience a problem with their device screen being oriented the wrong way during use [11]. In [11], the authors proposed iRotate, a previous solution which uses computer vision to solve the orientation problem. We propose iLieDown, an improved method of automatically rotating smartphones, tablets, and other device displays. This paper introduces a new algorithm to correctly orient the display relative to the user’s face using a convolutional neural network (CNN). The CNN model is trained to predict the rotation of faces in various environments through data augmentation, uses a confidence threshold, and analyzes multiple images to be accurate and robust. iLieDown is battery and CPU efficient, causes no noticeable lag to the user during use, and is 6x more accurate than iRotate.
ContributorsTallman, Riley Paul (Author) / Yang, Yezhou (Thesis director) / Fang, Zhiyuan (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-12