Authentication in mobile devices through hand gesture recognition

Abstract

This article proposes an innovative biometric technique based on the idea of authenticating a person on a mobile device by gesture recognition. To accomplish this aim, a user is prompted to be recognized by a gesture he/she performs moving his/her hand while holding a mobile device with an accelerometer embedded. As users are not able to repeat a gesture exactly in the air, an algorithm based on sequence alignment is developed to correct slight differences between repetitions of the same gesture. The robustness of this biometric technique has been studied within 2 different tests analyzing a database of 100 users with real falsifications. Equal Error Rates of 2.01 and 4.82% have been obtained in a zero-effort and an active impostor attack, respectively. A permanence evaluation is also presented from the analysis of the repetition of the gestures of 25 users in 10 sessions over a month. Furthermore, two different gesture databases have been developed: one made up of 100 genuine identifying 3-D hand gestures and 3 impostors trying to falsify each of them and another with 25 volunteers repeating their identifying 3-D hand gesture in 10 sessions over a month. These databases are the most extensive in published studies, to the best of our knowledge.

You Are How You Touch: User Verification on Smartphones via Tapping Behaviors

“…Guerra-Casanova et al [15] proposed a biometric technique based on the idea of authenticating a person on a mobile device by gesture recognition, and achieve Equal Error Rate (EER) between 2.01% and 4.82% on a 100-users base. Unobtrusive methods for authentication on mobile smart phones have emerged as an alternative to typed passwords, such as gait biometrics (achieving an EER of 20.1%) [13,26], or the unique movement users perform when answering or placing a phone call (EER being between 4.5% and 9.5%) [11].…”

Mid-air Authentication Gestures

“…Applications involving the Kinect include static hand gesture recognition [11,24], dynamic hand/arm gesture recognition [33], software control [25], surgeons assistance [15], and authentication [29]. Other sensors employed for (hand) gesture recognition include (smartphone integrated) accelerators [9,10,16], virtual reality interfaces like Cyber Glove [20], and specific biometric devices (e.g. a Smart Pen [1]).…”

“…Arm movements are captured for this purpose by (smartphone integrated) accelerators [9,10,16]. Mid-air handwriting recognition for authentication is conducted with different sensors (Kinect [29], Bio Smart Pen [1]); sign language recognition for identifying user-specific pass-codes is suggested in [12].…”

A Multimodal User Authentication System Using Faces and Gestures

“…Along with the popularization of various IT devices such as smart phones, Kinect, and stereo cameras, a number of studies have been conducted to show that gestures can be used as a good behavioral biometric signal for user authentication. In earlier studies [10–12], it was shown that accelerometer-based gesture recognition is feasible for user authentication in mobile devices. Also, in [13] the accelerometer and the gyroscope on mobile devices were combined for gesture-based user authentication.…”