A Novel Multiple Distances Based Dynamic Time Warping Method for Online Signature Verification

Lu, Xinyi; Fang, Yuxun; Wu, Qiuxia; Zhao, Junhong; Kang, Wenxiong

doi:10.1007/978-3-319-97909-0_68

Cited by 2 publications

(1 citation statement)

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“…Here, we introduce related research on biometrics authentication using hand gestures. For authentication by hand gesture, a method using an accelerometer [13,14] and a method using a color image or a depth image have been proposed. Here, the latter related method is introduced.…”

Section: Personal Authentication Using Hand Gesturesmentioning

confidence: 99%

Hand authentication from RGB-D video based on deep neural network

Ryogo¹,

Sasaki²,

Tsumura³

et al. 2022

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In recent years, behavioral biometrics authentication, which uses the habit of behavioral characteristics for personal authentication, has attracted attention as an authentication method with higher security since behavioral biometrics cannot mimic as fingerprint and face authentications. As the behavioral biometrics, many researches were performed on voiceprints. However, there are few authentication technologies that utilize the habits of hand and finger movements during hand gestures. Only either color images or depth images are used for hand gesture authentication in the conventional methods.In the research, therefore, we propose to find individual habits from RGB-D images of finger movements and create a personal authentication system. 3D CNN, which is a deep learning-based network, is used to extract individual habits. An Fmeasure of 0.97 is achieved when rock-paper-scissors are used as the authentication operation. An F-measure of 0.97 is achieved when the disinfection operation is used. These results show the effectiveness of using RGB-D video for personal authentication.

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Section: Personal Authentication Using Hand Gesturesmentioning

confidence: 99%