Biopen–Fusing password choice and biometric interaction at presentation level

The paper presents experiments with the home-made, low-cost prototype of a sensor-equipped pen for handwriting-based biometric authentication. The pen allows to capture the dynamics of user writing on normal paper, while producing a kind of password (passphrase) chosen in advance. The use of a word of any length instead of the user's signature makes the approach more robust to spoofing, since there is no repetitive pattern to steal. Moreover, if the template gets violated, this is much less harmful than signature catch. The entailed sensors are a pair of accelerometer and gyroscope and a pressure sensor. The aim is a natural yet precise interaction, that allows recognizing the user by the signals recorded while producing a specific word chosen during enrollment and possibly changed later. The pen can be exploited in a number of applications requiring user recognition, yet relieving from the need to learn complex procedures, and to undergo critical capture operations. The approach fuses the use of a kind of password, though not necessarily complex as those requested by traditional approaches, and biometric recognition. The novelty with respect to most proposals in literature is the combination of three elements at once: the matching of any handwritten text instead of user signature, the on-line capture of seven sensor signals to recognize handwriting dynamics (three from accelerometer, three from gyroscope and one from pressure sensor), and the use of normal paper instead of a digitizing tablet. Presented experiments test two different recognition techniques, implemented by two modules that can be alternatively plugged into the system. An SVM-based verification module entails to extract the most relevant features from writing dynamics, and to acquire a sufficient amount of enrolling data (30 samples per user) to train an SVM for each user. A pure Dynamic Time Warping (DTW) verification module does not require such training, and is tested using either a gallery with the same number of templates per user as those used for SVM training, or with a gallery containing a much lower number of templates per user (namely 5). Obtained results encourage further investigation of lightweight strategies for written password dynamics recognition