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Based on the captured video stream, we develop an algorithm to extract 1) features for user tracking, including body keypoints and their temporal and spatial association, near field hand status, and 2) features for user identity assignment. We install a fisheye camera on the top of the phone to achieve a panoramic vision that can capture both user’s body and on-screen hands. To instantiate the Auth+Track model, we present PanoTrack, a prototype that integrates body and near field hand information for user tracking. By sparse authentication and continuous tracking of the user’s status, Auth+Track eliminates the “gap” authentication between fragmented sessions and enables “Authentication Free when User is Around”. We propose Auth+Track, a novel authentication model that aims to reduce redundant authentication in everyday smartphone usage.

Auth+Track: Enabling Authentication Free Interaction on Smartphone by Continuous User Tracking We then test this model with LipType and other speech and silent speech recognizers to demonstrate its effectiveness.

We then develop an independent repair model that processes video input for poor lighting conditions, when applicable, and corrects potential errors in output for increased accuracy. To address these, first, we develop LipType, an optimized version of LipNet for improved speed and accuracy. Developing new recognizers and acquiring new datasets is impractical for many since it requires enormous amount of time, effort, and other resources. Lip reading can mitigate many of these challenges but the existing silent speech recognizers for lip reading are error prone. Speech recognition is unreliable in noisy places, compromises privacy and security when around strangers, and inaccessible to people with speech disorders.

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