A biometric authentication model using hand gesture images

Abstract

A novel hand biometric authentication method based on measurements of the user’s stationary hand gesture of hand sign language is proposed. The measurement of hand gestures could be sequentially acquired by a low-cost video camera. There could possibly be another level of contextual information, associated with these hand signs to be used in biometric authentication. As an analogue, instead of typing a password ‘iloveu’ in text which is relatively vulnerable over a communication network, a signer can encode a biometric password using a sequence of hand signs, ‘i’ , ‘l’ , ‘o’ , ‘v’ , ‘e’ , and ‘u’. Subsequently the features from the hand gesture images are extracted which are integrally fuzzy in nature, to be recognized by a classification model for telling if this signer is who he claimed himself to be, by examining over his hand shape and the postures in doing those signs. It is believed that everybody has certain slight but unique behavioral characteristics in sign language, so are the different hand shape compositions. Simple and efficient image processing algorithms are used in hand sign recognition, including intensity profiling, color histogram and dimensionality analysis, coupled with several popular machine learning algorithms. Computer simulation is conducted for investigating the efficacy of this novel biometric authentication model which shows up to 93.75% recognition accuracy.

Biometric Multi-Factor Authentication Scheme in Cloud Computing

“…The idea of this paper starts from Frong, Zhuang and Fister (2013). The authors describe an imagebased, biometric authentication model for hand gestures captured by video recording [9]. The gestures serve as what the authors refer to as a "biometrics password" and provide context for biometric feature extraction and biometric matching based on the "hand shape and the postures in doing those signs" [10].…”

Authentication systems: principles and threats

“…The system accomplishes an EER that equals to 11.71% using a leap motion 3D controller for the motion acquisition process. While (Fong, Zhuang, & Fister, 2013) have utilized sign language, captured by an ordinary video camera, as hand gestures for biometric authentication system. They found that the system is able to verify individuals with maximum accuracy of 93.75%, assessing the feasibility of using gestures for authentication as well as for message communication.…”

Interactive 180° Rear Projection Public Relations

“…More challenges and applications can be found e.g. in [4,7,8,12]. Of course, interaction can be done with other 3D sensors, such as the mentioned Leap Motion, an example can be found in [6] where the interaction is done with holograms for teaching technical drawing.…”