Multi-Biometrics: Fusing At The Classification Output Level Using Keystroke and Mouse Motion Features Todd Breuer, Paola Garcia Cardenas, Anu George, Hung Hai Le, Leigh Anne Clevenger and Vinnie Monaco Capstone Project - Team 6 Page 1

Introduction Fusing multiple biometrics using different fusion strategies and two features, mouse motion and keystroke, to improve the accuracy and the percentage of authenticating users. Biometric Trait EER Motion 1.14% Click 33.49% Scroll 36.27% Keystroke 24.35% Biometric Traits Fused EER Keystroke and Mouse Motion 3.9% Background on last semester’s paper Last semester’s results Page 2 Last semester’s results

Levels of Fusion Pre-Classification Level Sensor or Signal Level Feature Level Post-Classification Level Matching or Measurement or Confidence level Rank Level Decision Level Good night guys! Page 3

Methodology By using different python classification techniques and fusion methods that sklearn provides, the data can be manipulated, classified and combined to design a better multi-biometric system. All data collection process was done in a closed system. It is important to note that the same raw data from last semester’s research was used with our research, in order to compare the accuracy and EER% of the features more precisely Page 4

Methodology cont... Classifier matching score data from keystroke and mouse motion, and their fused output after the Mean (Average) method was applied. Page 5

Fusion Strategies Used Maximum P=Max(P1,P2,....Pj) Minimum P=Min(P1,P2,....Pj) Mean P=Mean(P1,P2,....Pj) Median P=Med(P1,P2,....Pj) Page 6

Research Tools We used python sklearn as our implementation method for the K-nearest neighbors. To run the python script we supply the two datasets, mouse motion and the keystroke features. This results in the highest possible percentage of authenticating the users. Page 7

Classification Accuracy Results Accuracy percentages were first obtained by fusing the keystroke and mouse motion data. Fusion Methods Classification Accuracy Maximum 73% Mean 86% Median Minimum 77% Page 8

Results cont... EER values were obtained from the ROC curves when the False Acceptance Rate and the False Rejection Rate are equal. FAR = FRR Fusion Methods EER Maximum 13.4% Mean 12.5% Median Minimum 18.8% Page 9

Results cont... ROC curve of all four fusion methods in study. Page 10

Conclusions As predicted, by fusing two of the top performers from Pre-Classification, the accuracy increases. Though we did not meet our EER target of less than 1.13 EER percentage. in this slide its post-classification not Pre-classification right? Page 11

Future Work The results presented in this study suggest that multi-biometric systems improve performance over that of individual biometric systems, however, more features should be tested applying different methods. Page12

Future Work cont... There are a total of four levels in biometric fusion. It would be good to use the data from our match score level and apply it to the decision level (3rd level) in order to find out if higher accuracy rates and lower ERR% can be obtained. Page 13

Future Work cont... It would be useful to experiment using the scroll and the click features along with the fusion methods presented above as well. Being able to determine which data works better with different fusion methods at different fusion levels would be crucial on the design of an effective multi-biometric system. Page 14

Thank you! Questions? Page 15