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.
Highlights
The goal of biometric authentication is the automated verification of identity of a living person by proving over some unique feature which only he possesses
As a technical contribution by this paper, we evaluate the performance of different classifiers pertaining to the proposed hand gesture biometric authentication model
We proposed a novel biometric discipline that uses hand sign gestures as captured in static images in signing
Summary
The goal of biometric authentication is the automated verification of identity of a living person by proving over some unique feature which only he possesses. The motions measured from hand gesture are shown to give rise to linguistic information better than static postures alone [24] Another emerging trend as observed from the literature is the hybrid use of features that are extracted from different biometric sources. Each student repeated in posing at slightly different angles In this set of data which are subject to training and testing the classifier methods, the hand contour is extracted as a feature which was treated by scaling and removal of Figure 7 Hand gesture image of letter ‘p’ that has the directional lines added for illustration. The Kappa statistics is computed here from the 10-fold cross-validation with each fold of different combination of partitions (training and testing) as different inter-observers In pattern recognition such as hand sign recognition in biometric authentication, precision rate or just Precision is the fraction of relevantly recognized instances. Whereas a recall of score 1.0 means that each instance from that particular class is labeled to this class and all are predicted correctly, none shall be left out
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