Haptic passwords

Haptic technologies have made it possible for human users to interact with cyber systems not only via traditional interfaces like keyboards and mice but also by applying force and motion. With these extra information channels, how a user haptically interacts with a system potentially presents unique user dependent features and can thus be used for authentication purposes. In this paper, we propose a new biometric technology based on haptic interaction. Our technique leverages artificial neural network (ANN) based wavelet analysis to perform user identification. Identification and authentication are done in two steps: a discrete wavelet transform (DWT) is applied to extract features, and then the neural network is used to perform identification and authentication. The performance of the model is evaluated based on identification and authentication accuracies. The results show that our proposed haptic password system has a high identification accuracy and that it is resistant to forgery attacks.

Authors

• Junjie Yan
• Kevin Huang 0001
• Tamara Bonaci
• Howard Jay Chizeck

Keywords

• Haptic interfaces
• Discrete wavelet transforms
• Authentication
• Feature extraction
• Forgery
• Neural Network
• Artificial Neural Network
• Biometric
• Wavelet Transform
• User Identification
• Force Feedback
• Human Users
• High Identification Accuracy
• Virtually
• Right-hand
• Training Set
• Receiver Operating Characteristic Curve
• Fingerprint
• Support Vector Machine
• Classification Performance
• Recurrent Neural Network
• Human-computer Interaction
• Velocity Data
• Coefficient Of Level
• Mother Wavelet
• Authentication System
• Right-handed Subjects
• User's Hand
• Authentication Method
• Artificial Neural Network Classifier
• User Authentication
• Teleoperation System