  Most of the current user authentication systems are based on PIN code,
password, or biometrics traits which can have some limitations in usage and
security. Lifestyle authentication has become a new research approach. A
promising idea for it is to use the location history since it is relatively
unique. Even when people are living in the same area or have occasional travel,
it does not vary from day to day. For Global Positioning System (GPS) data, the
previous work used the longitude, the latitude, and the timestamp as the
features for the classification. In this paper, we investigate a new approach
utilizing the distance coherence which can be extracted from the GPS itself
without the need to require other information. We applied three ensemble
classification RandomForest, ExtraTrees, and Bagging algorithms; and the
experimental result showed that the approach can achieve 99.42%, 99.12%, and
99.25% of accuracy, respectively.
