Thesis - Instituto de Telecomunicações

116 CHAPTER 6. APPLICATIONS AND RESULTSn. users feature10 paused time9 duration8 max(v y )7 jitter6 min(v)6 max( ˙v)Table 6.1: The 6 most frequently used features.The test function returns the equal error rate, given N sequences of strokes of length l,using the classifier tuned for user i. The input sequence of strokes for a test is composedof N/2 strokes randomly sampled from the user’s testing set, and N/2 strokes randomlysampled from the testing sets of all the other users.One of the free variables of the system is the number of strokes that will be used in theverification task. The final results were obtained using a test that was run 10000 times foreach stroke sequence length from 1 to 50 strokes. The mean time of a stroke is approximately1 second. The values associated with the test using 10 strokes require approximately 10seconds of interaction.6.1.2 Feature SelectionAccording to the user-tuned feature selection scheme presented in section 5.2, the featuressubset that best identified each user were selected. Almost all features were used (58 fromthe total of 64) and the feature vector had different lengths for different users, ranging from2 to 10 features. This can be observed from the histogram of feature vector sizes for all theusers, presented in figure 6.1.The average size of the feature vector is 6. The most frequently used features for theseveral users are listed in table 6.1.6.1.3 Authentication ResultsIn table 6.2 we present average results of the equal error rate for all 50 users for several strokesequence lengths. A graphical display of these results is shown in figure 6.2 were sequencelength was converted to time in seconds. As shown, the mean value (continuous line) and