GesID: 3D Gesture Authentication Based on Depth Camera and One-Class Classification.

ABSTRACT: Biometric authentication is popular in authentication systems, and gesture as a carrier of behavior characteristics has the advantages of being difficult to imitate and containing abundant information. This research aims to use three-dimensional (3D) depth information of gesture movement to perform authentication with less user effort. We propose an approach based on depth cameras, which satisfies three requirements: Can authenticate from a single, customized gesture; achieves high accuracy without an excessive number of gestures for training; and continues learning the gesture during use of the system. To satisfy these requirements respectively: We use a sparse autoencoder to memorize the single gesture; we employ data augmentation technology to solve the problem of insufficient data; and

SUBMITTER: Wang X 

PROVIDER: S-EPMC6210771 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature