Available at: http://digitalcommons.calpoly.edu/theses/1216
Date of Award
MS in Computer Science
Studies show that smartphone thefts are a significant problem in the United States.  With many upcoming proposals to decrease the theft-rate of such devices, investigating new techniques for preventing smartphone theft is an important area of research. The prevalence of new biometric identification techniques for smartphones has led some researchers to propose biometric anti-theft measures for such devices, similar to the current fingerprint authentication system for iOS. Gait identification, a relatively recent field of study, seems to be a good fit for anti-theft because of the non-intrusive nature of passive pattern recognition in walking. In this paper, we reproduce and extend a modern gait recognition technique proposed in Cell Phone-Based Biometrics by testing the technique outside of the laboratory on real users under everyday conditions. We propose how this technique can be applied to create an anti-theft system, and we discuss future developments that will be necessary before such research is ready to be implemented in a release-quality product. Because previous studies have also centered around the ability to differentiate between individual users from a group, we will examine the accuracy of identifying whether or not a specific user is currently using a system. The system proposed in this paper shows results as high as 91% for cross-fold accuracy for some users; however, the predictive accuracy for a single day’s results ranged from 0.8% accuracy to 92.9% accuracy, showing an unreliability that makes such a system unlikely to be useful under the pressure of real-world conditions.