# Continuous Mobile User Authentication Using Combined Biometric Traits
## 6 Evaluation and Results
### Participants and Data  
For training the HMM classifier, data were collected from six different participants. The users ranged between 18 and 50 years with an equal amount of males and females. All of them stated that they regularly use their smartphones and are comfortable with the provided Android device. Basic navigation functionality was explained to each of them, in order to be able to, for example, move to the previous page or go to the home screen of the device. Those actions are achieved using hardware buttons. With our test device, an OnePlus 6T, those buttons were mapped to on-screen gestures. So, for example, to navigate back, the user has to swipe up from the right bottom corner of the device. Furthermore, all users were asked to use the device just as they would normally do when browsing the web. They were instructed to use the Chrome Web Browser ([[https://play.google.com/store/apps/details?id=com.android.chrome](https://play.google.com/store/apps/details?id=com.android.chrome)]([https://play.google.com/store/apps/details?id=com.android.chrome](https://play.google.com/store/apps/details?id=com.android.chrome)), accessed on 4 December 2021) and do not interact with any other applications. Additionally, the recording software was started for them and the internet browser opened with an empty tab. Because of this fact, it had to be considered that for each user the first three gestures were discarded, because they were always done by the instructor and would distort the gained results.  
The amount of gestures that were captured per user ranged between 195 and 1791 strokes. However, the dataset with 1791 entries is an outlier, since this is the one we are using to train the authentication classifier on, while the other datasets are primarily used for testing the performance of it. When only considering the datasets used for evaluation and testing, they are composed of about 375 gestures on average. Those numbers represent all gestures, except ones that only consist of a single position entry, because those would not represent a sequence of input. Additionally, gestures with really long sequences were discarded in some training instances as well, since maybe users keep their finger on the phone when cleaning the display which would be captured as an input gesture, but in fact, is none. How many gestures were finally used in the model training and verification process, as well as what inputs were discarded, is discussed in more detail later.  
For every gesture, a sequence of variable length is captured, including an identifier to know which data points belong to each gesture, the position on the display, accelerometer sensor data, and a timestamp indicating when the position was captured. For the training of the authentication model, different combinations of those available features were used. This was done because some of the features may correlate with each other, meaning that they do not help in providing more information for the classifier. Furthermore, different forms of data normalization and feature extraction of existing data were done to improve the accuracy of the created classifier.