Biometric-rich gestures

TLDR

The paper proposes a novel multi‑touch gesture‑based authentication technique. The authors developed a five‑finger gesture set and trained a pattern‑recognition classifier on biometric movement data collected from a multi‑touch surface. The system achieved 90 % accuracy for single gestures, improved with gesture sequences, and user satisfaction correlated with recognition performance, indicating strong potential for multi‑touch gesture authentication.

Abstract

In this paper, we present a novel multi-touch gesture-based authentication technique. We take advantage of the multi-touch surface to combine biometric techniques with gestural input. We defined a comprehensive set of five-finger touch gestures, based upon classifying movement characteristics of the center of the palm and fingertips, and tested them in a user study combining biometric data collection with usability questions. Using pattern recognition techniques, we built a classifier to recognize unique biometric gesture characteristics of an individual. We achieved a 90% accuracy rate with single gestures, and saw significant improvement when multiple gestures were performed in sequence. We found user ratings of a gestures desirable characteristics (ease, pleasure, excitement) correlated with a gestures actual biometric recognition rate - that is to say, user ratings aligned well with gestural security, in contrast to typical text-based passwords. Based on these results, we conclude that multi-touch gestures show great promise as an authentication mechanism.