A 3-D Hand Gesture Signature Based Biometric Authentication System for Smartphones

Abstract

The authentication mechanism being equipped in most of the smartphones, by detecting a 4-digit password or a simple pattern, are easy to be hacked and impersonated. In this paper, a 3-D hand gesture signature (HGS) based biometric authentication system is designed and implemented by taking advantage of the on-phone accelerometer to capture the 3-D acceleration information when user makes a gesture to gain access to the phone. The captured data will be processed through a sequence of signal processing such as data smooth, gesture spotting, sequence alignment, and interpolation, and then a match rule will be used to compare the processed data and the genuine user's registered pattern to determine whether granting the access to the phone to the user. And an automatic template updating strategy based on cluster analysis is proposed to improve the stability of the system. The 3-D HGS authentication system has been implemented on real smartphones, and the results tested for a total of 76 520 times by 19 users show very low false acceptance (0.27%) and false rejection rates (4.65%). Furthermore, comparison tests have been carried out among the 3-D HGS and two similar authentication systems by exporting the real gesture samples from the phones to a desktop PC, the simulation results reveal the 3-D HGS system has the best authentication accuracy.

Hand Gesture Recognition on a Resource-Limited Interactive Wristband

“…As an intuitive and convenient expression, gesture recognition becomes ubiquitous in human-computer interaction. Gesture recognition technology has been widely used in robot control [ 1 ], military tasks [ 2 ], authentication systems [ 3 ], medical assistance [ 4 ], smart home [ 5 ], and games [ 6 ]. Recently with the rapid development of consumer electronics and the proliferation of technologies, further cutting down computational resources under the premise of ensuring accuracy has become a new requirement.…”

“…Most of the widely-used methods, such as support vector machine (SVM) [ 5 , 8 , 9 , 10 ], hidden Markov model (HMM) [ 11 , 12 , 13 ], neural network [ 14 , 15 , 16 , 17 ], and dynamic time warping (DTW) [ 3 , 18 , 19 , 20 , 21 , 22 ] have achieved good results for recognition accuracy, from 90% to 100%. Traditional machine learning (ML)and neural network (deep learning, DL) methods are data-driven.…”

“…Their recognition performance depends on whether the training data is sampled adequately for the scene in which they will be used. DTW is considered the best accuracy/computation cost relationship [ 3 ] and has been widely applied to speech recognition, gesture recognition, and other signal recognition tasks with time sequence characteristics. However, the main challenge for all above algorithms lies in the misrecognition caused by different preferred speeds and styles, manifested as individual differences, e.g., Y. Wang et al [ 10 ] got the accuracy of recognition at 100% under user-dependent cases, while only 87% accuracy under user-independent cases.…”

“…However, this level of computing still has relatively high requirements for CPU (Qualcomm Snapdragon 810, for example), which make it difficult to run on a very low-cost MCU, such as Cortex32-M0 as used in this paper. In fact, almost all the studies implement their HGR algorithm on powerful computing devices, such as PC [ 7 , 9 , 10 , 13 , 14 , 18 , 19 , 22 ], smartphone [ 3 , 21 ], or FPGA [ 15 ]. The difficulties lie in compromising both on the hardware cost and the algorithm performance.…”

Security analysis and psychological study of authentication methods with PIN codes

“…Evangelin and Fred proposed a method using physical characteristics recognition [11], while Sun et al proposed an authentication method based on a hand gesture signature [29].…”