Online signature verification technologies, such as those available in banks and post offices, rely on dedicated digital devices such as tablets or smart pens to capture, analyze and verify signatures. In this paper, we suggest a novel method for online signature verification that relies on the increasingly available hand-worn devices, such as smartwatches or fitness trackers, instead of dedicated ad-hoc devices. Our method uses a set of known genuine and forged signatures, recorded using the motion sensors of a hand-worn device, to train a machine learning classifier. Then, given the recording of an unknown signature and a claimed identity, the classifier can determine whether the signature is genuine or forged. In order to validate our method, it was applied on 1980 recordings of genuine and forged signatures that we collected from 66 subjects in our institution. Using our method, we were able to successfully distinguish between genuine and forged signatures with a high degree of accuracy (0.98 AUC and 0.05 EER).
Wrist-worn devices, such as smartwatches and fitness trackers, have become perhaps the most popular category of wearable devices, and many major manufacturers, including Samsung and Apple, have released their devices recently. Since these devices are worn on the wrist, they introduce a unique opportunity to both detect and understand a user's arm, hand and finger movements as shown in @cite_17 . In comparison, forearm devices such as the one presented in @cite_13 , are very limited in detecting fine gestures such as finger gestures or writing. Similarly, finger worn devices such as the one introduced in @cite_20 , can be used to understand users' finger gestures and writing. However, this is limited to the gestures of a specific finger, and gestures using other fingers can not be identified. Wrist-worn devices are less limited as they facilitate gesture recognition based on the arm, the hand and all of the fingers.
@cite_13 @cite_20 @cite_17
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This paper presents a remote gesture input solution for interacting indirectly with user interfaces on mobile and wearable devices. The proposed solution uses a wearable ring platform worn on users index finger. The ring detects and interprets various gestures performed on any available surface, and wirelessly transmits the gestures to the remote device. The ring opportunistically harvests energy from an NFC-enabled phone for perpetual operation without explicit charging. We use a finger-tendon pressure-based solution to detect touch, and a light-weight audio based solution for detecting finger motion on a surface. The two level energy efficient classification algorithms identify 23 unique gestures that include tapping, swipes, scrolling, and strokes for hand written text entry. The classification algorithms have an average accuracy of 73 with no explicit user training. Our implementation supports 10 hours of interactions on a surface at 2 Hz gesture frequency. The prototype was built with off-the-shelf components has a size similar to a large ring. Smartwatch is becoming one of the most popular wearable device with many major smartphone manufacturers such as Samsung and Apple releasing their smartwatches recently. Apart from the fitness applications, the smartwatch provides a rich user interface that has enabled many applications like instant messaging and email. Since the smartwatch is worn on the wrist, it introduces a unique opportunity to understand user's arm, hand and possibly finger movements using its accelerometer and gyroscope sensors. Although user's arm and hand gestures are likely to be identified with ease using the smartwatch sensors, it is not clear how much of user's finger gestures can be recognized. In this paper, we show that motion energy measured at the smartwatch is sufficient to uniquely identify user's hand and finger gestures. We identify essential features of accelerometer and gyroscope data that reflect the movements of tendons (passing through the wrist) when performing a finger or a hand gesture. With these features, we build a classifier that can uniquely identify 37 (13 finger, 14 hand and 10 arm) gestures with an accuracy of 98 . We further extend our gesture recognition to identify the characters written by the user with her index finger on a surface, and show that such finger-writing can also be accurately recognized with nearly 95 accuracy. Our presented results will enable many novel applications like remote control and finger-writing-based input to devices using smartwatch.