One of the latest authentication methods is by discerning human gestures. Previous research has shown that different people can develop distinct gesture behaviours even when executing the same gesture. Hand gesture is one of the most commonly used gestures in both communication and authentication research since it requires less room to perform as compared to other bodily gestures. There are different types of hand gesture and they have been researched by many researchers, but stationary hand gesture has yet to be thoroughly explored. There are a number of disadvantages and flaws in general hand gesture authentication such as reliability, usability, and computational cost. Although stationary hand gesture is not able to solve all these problems, it still provides more benefits and advantages over other hand gesture authentication methods, such as making gesture into a motion flow instead of trivial image capturing, and requires less room to perform, less vision cue needed during performance, and so forth. In this paper, we introduce stationary hand gesture authentication by implementing edit distance on finger pointing direction interval (ED-FPDI) from hand gesture to model behaviour-based authentication system. The accuracy rate of the proposed ED-FPDI shows promising results.
Authentication has three basic factors—knowledge, ownership, and inherence. Biometrics is considered as the inherence factor and is widely used for authentication due to its conveniences. Biometrics consists of static biometrics (physical characteristics) and dynamic biometrics (behavioral). There is a trade-off between robustness and security. Static biometrics, such as fingerprint and face recognition, are often reliable as they are known to be more robust, but once stolen, it is difficult to reset. On the other hand, dynamic biometrics are usually considered to be more secure due to the constant changes in behavior but at the cost of robustness. In this paper, we proposed a multi-factor authentication—rhythmic-based dynamic hand gesture, where the rhythmic pattern is the knowledge factor and the gesture behavior is the inherence factor, and we evaluate the robustness of the proposed method. Our proposal can be easily applied with other input methods because rhythmic pattern can be observed, such as during typing. It is also expected to improve the robustness of the gesture behavior as the rhythmic pattern acts as a symbolic cue for the gesture. The results shown that our method is able to authenticate a genuine user at the highest accuracy of 0.9301 ± 0.0280 and, also, when being mimicked by impostors, the false acceptance rate (FAR) is as low as 0.1038 ± 0.0179.
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