Dynamic-Hand-Gesture Authentication Dataset and Benchmark
Chang Liu, Yulin Yang, Xingyan Liu, Linpu Fang, Wenxiong Kang
Abstract
In recent years, biometrics have received considerable attention for its reliability and usability. Dynamic-hand-gesture is one of the representative biometric modalities, with advantages of safety and template-replaceability, has huge potential value. However, due to the lack of large-scale dataset and comprehensive evaluation methods, few researches are intended to study the dynamic-hand-gesture authentication method. In this article, we introduce a new dataset SCUT-DHGA, which is the first large-scale Dynamic-Hand-Gestures-Authentication dataset. SCUT-DHGA contains 29,160 dynamic-hand-gesture video sequences and more than 1.86 million frames for both color and depth modalities acquired from 193 volunteers. Six kinds of dynamic-hand-gestures are carefully designed for researching two types of authentication tasks: gesture-predefined authentication and gesture-free authentication. To investigate the hypothesis that users' gestures would be variant after time-span, which will degrade the performance of a dynamic-hand-gesture authentication system, two separate sessions' data were acquired from 50 volunteers with an average interval of one week. Beside the SCUT-DHGA dataset, we also benchmark this dataset with our proposed DHGA-net. By releasing such a large-scale dataset and benchmark, we expect dynamic-hand-gesture authentication methods to gain further improvement and generalization.