Gesture Input as an Out-of-band Channel

Abstract

Abstract-In recent years, there has been growing interest in secure pairing, which refers to the establishment of a secure communication channel between two mobile devices. There are a number of descriptions of the various types of out-of-band (OOB) channels, through which authentication data can be transferred under a user's control and involvement. However, none have become widely used due to their lack of adaptability to the variety of mobile devices. In this paper, we introduce a new OOB channel, which uses accelerometer-based gesture input. The gesture-based OOB channel is suitable for all kinds of mobile devices, including input/output constraint devices, as the accelerometer is small and incurs only a small computational overhead. We implemented and evaluated the channel using an Apple iPhone handset. The results demonstrate that the channel is viable with completion times and error rates that are comparable with other OOB channels.

Advanced Machine Learning for Gesture Learning and Recognition Based on Intelligent Big Data of Heterogeneous Sensors

“…Moreover, active developments in gesture recognition techniques have led to the recent developments in virtual reality (VR) technology [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. The gesture recognition accuracy, which is a key metric to evaluate the effectiveness of gesture recognition systems, has witnessed steady improvements using input data from multiple sensors [17][18][19][20][21][22]. Moreover, researchers have studied visual recognition and learning systems for more intuitive learning of gesture recognition.…”

“…First, in the gesture registration stage, the proposed method obtains gesture data from the multi-sensor fusion. Using two or more sensors, various types of gestures can be learnt and precise and accurate gesture recognition can be realized [17][18][19][20][21][22]. Moreover, the proposed method allows the end user to limit the range of gestures to be registered for recognizing specific body parts, thereby improving the recognition accuracy.…”

Accumulative Secure Group Association in an Ad-hoc and Ubiquitous Network (Version 3.0)

“…However, it has to be the same or similar for all group members, so that SAS authentication data can be verified simultaneously. OOB channels may be divided into three main categories based on the user involvement; device-to-device (d2d), such as barcode-based [7], two device-to-human (2 x d2h), such as LED light-based [7], and device-to-human + human-to-device (d2h+h2d), such as gesturebased [12]. In d2d type of OOB channel, the users have relatively little influence on the success of the transmission.…”

“…The verifier reads the barcode c 2015 Information Processing Society of Japan using a camera. Chagnaadorj et al developed a gesture-based OOB channel [12]. The requester converts the authentication data into a sequence of gestures and displays them.…”

“…Vaudenay et al introduced a SAS (Short Authenticated String) protocol [5], which is well suited for many low bandwidth OOB channels as it reduces the authentication data to 15 bits while providing a reasonable level of security. To date, a significant number of OOB channels [6], [7], [8], [9], [10], [11], [12] have been proposed as well. To elucidate how the authentication data is transferred via an OOB channel, we will look at two of them briefly.…”