A statistical approach for person verification using human behavioral patterns

Abstract

We propose a person verification method using behavioral patterns of human upper body motion. Behavioral patterns are represented by three-dimensional features obtained from a time-of-flight camera. We take a statistical approach to model the behavioral patterns using Gaussian mixture models (GMM) and support vector machines. We employ the maximum likelihood linear regression adaptation method to estimate GMM parameters with a limited amount of data. Experimental results show that it reduced by 28.6% the relative equal error rates from a system using the maximum likelihood estimation with 25 samples per subject. We also demonstrate that the proposed approach is robust against variations in body motion over time.

Behavioral Biometrics

“…Recently, a shift has been made towards user profiling in a graphical environment such as Windows as most users prefer convenience of a Graphical User Interface (Cugler, Yaguinuma, & Santos). Typical features extracted from the user's interaction with a windows based machine include: time between windows, time between new windows, number of windows simultaneously open, and number of words in a window title (Goldring, 2003;Kaufman, Cervone, & Michalski, 2003) They track the motion of skin pores on the face during a facial expression and obtain a vector field that characterizes the deformation of the face. In the training process, two high-resolution images of an individual, one with a neutral expression and the other with a facial expression, like a subtle smile, are taken to obtain the deformation field (Mainguet, 2006); • Email Behavior: Email sending behaviour is not the same for all individuals.…”

“…While some research in that area has been done, particularly with command line interfaces (Roy A. Maxion & Tahlia N. Townsend, 2002;Schonlau et al, 2001) and more recently with point and click interfaces (Goldring, 2003) much more can be accomplished. Usually lowlevel side effects of user activity are all that is taken to generate a user profile (Yampolskiy, 2007b).…”

“…Usually lowlevel side effects of user activity are all that is taken to generate a user profile (Yampolskiy, 2007b). For example, one study concentrated on things like number of open windows, time between new windows and number of words in a window title (Goldring, 2003). As the technology advances, it may become possible to use higher-level behaviours to generate more accurate user profiles:…”