User recognition based on periocular biometrics and touch dynamics

Lucia Cascone

Abstract:

Web user behavioural recognition is the process by which web users are identified and distinguished
through behavioural features. In this work, two sources of behavioural biometric data are analyzed for
the development of this web user identification model, touch dynamics and the characteristics extracted
from the periocular area related to the pupils, blinks and fixations. The approach adopted used to improve
the overall performance of the multimodal biometric recognition system is based on a fusion at the Feature level to which different distance measure techniques (Euclidean, Bray-Curtis, Manhattan, Canberra,
Chebyshev, Cosine) are applied to determine if the test sample belongs to the target subject. To further
improve the system performance, we have applied multi-data processing methods such as Canonical Correlation Analysis (CCA) and Principal Component Analysis (PCA). The results obtained demonstrate the
promise of these two different biometric traits and, above all, of their fusion. In fact, the fusion approach
allows obtaining an accuracy higher than that of individual biometrics, reaching an accuracy of over 92%.