Manual For Biometric User Authentication on Smartphone Accelerometer Sensor Data
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1 PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR 1 Manual For Biometric User Authentication on Smartphone Accelerometer Sensor Data Noufal Kunnathu, Seidenberg School of CSIS, Pace University This document covers detailed instructions for re-producing the experiments described in the Technical paper. This paper assumes that the reader has some familiarity with WEKA. A. Load Data The first step is to prepare the input data for the model from the data vectors provided on Appendix-1. (This is the data that is derived from the raw data collected as part of the experiment. Please see the included excel file more information). Copy the CSV (Comma-Separated Values) data from Appendix-1 to a text file call this data.csv Open Weka. It gives following welcome screen. Click on Simple CLI button. This opens a Command Line Interface (CLI).
2 PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR 2 Enter the following command on the bottom text input box and click enter. $ java weka.core.converters.csvloader data.csv > data.arff This will create a file called data.arff in the current directory. Close the command-line interface and go back to the welcome screen. Now, click on Explorer to open the main user interface screen shown in Figure-2. Click the button Open file, browse and select the file called data.arff. At this point, 15 attributes listed Figure-3 will be loaded into the screen. In order to do the fir Regarding the suffixes on attribute names avg stands for Average, var stands for Variance, gvty stands for Gravity acceleration and lnr stands for Linear acceleration.
3 PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR 3 B. Experiment-1 For the first experiment, we don t need the variances, gravity, linear acceleration measurements. Hence, select these attributes and click Remove. This leaves us with following attributes. Fig. 4 Attributes for experiment-1. In order to evaluate the relevance of each of these attributes with respect to predicting the user instance an Attribute Evaluator is used. Click on Select Attributes on the top bar of Weka explorer interface. This opens following screen. Fig. 5 Attribute Selection. Click on Choose for the Attribute Evaluator algorithm. Following algorithms shows up.
4 PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR 4 Select ChiSquaredAttributeEval and click Yes on the following prompt. Make sure that User attribute is pre-populated for the instance class as shown below. Also Use full training set
5 PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR 5 Now, click on the Start button. As shown in the screenshot below, algorithm selected all the attributes with Z_avg having highest significance and Session_Id having lowest significance. It is time now time to run the classifier algorithm. Click on Classify from the top bar on the Explorer window.
6 PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR 6 Choose MultilayerPerceptron algorithm as shown below. Change Cross-validation count to 35. Make sure that User is selected as the target for classification, as shown below. Click on Start. This will execute the classifier and produce the results as shown below. For the first run, all attributes that are part of the feature set for the first experiment is included. It can be observed that the model has a performance of 77.14%
7 PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR 7 To find the smallest feature set that gives the best performance, remove the least significant attribute, which in this case is the session_id. This can be done on the Preprocess screen as described earlier. Re-run the classifier to get following results.
8 PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR 8 There is no change in the number of correctly classified instances or Kappa statistics. However, there is an improvement in the error metrics. Continue this process by removing the next least significant attribute as determined by the Chi-squared Attribute Evaluator. In this case, it is the Ear_Used attribute. Remove this attribute on the Preprocess screen to get following results. Interestingly the results have improved. Continue the process by removing the next least significant attribute. In this experiment, it is the Y_avg. As shown below, the performance deteriorated by removing Y_avg.
9 PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR 9 Hence we established the feature set for this experiment as following attributes The model gives a performance 80% with the kappa statistics of This concludes Experiment-1. C. Experiment-2 In order to start Experiment-2, go back to the main screen by clicking on the Preprocess button from the top bar. Click multiple-times the Undo button from the top of this screen until all the attributes in the dataset is restored. For this experiment, averages and variances are needed. Hence other attributes (gravity and linear acceleration) are removed. We are left with the following attributes for this experiment. Continue all the steps described for Experiment-1 to evaluate the attributes and to eliminate the least significant attributes to come up with the smallest set of attributes that provides the best performance. Following screen shows the final results.
10 PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR 10 Experiment-2 didn t classify any more instances correctly. However, there is improvement in error metrics for experiment-2. Experiment-2 determined following attributes as the feature set. D. Experiment-3 For Experiment-3, remove all except following attributes. Execute the steps as described earlier. The results are shown below.
11 PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR 11 It shows 82.85% success in correctly classifying the instances. So, it seems that phone picking-up model is out-performs the phone on-ear model. Following attributes are selected as part of the feature set. E. Experiment-4 This is the combined experiment. All attributes are considered for this experiment. The result screenshot is given below. It can be observed that the combined experiment has the best performance. It correctly classified 32 of the 35 instances with an impressive performance of 91.42%
12 PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR 12 The final feature set selected by this experiment is following
13 PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR 13 Appendix-1 Session_Id,Ear_Used,X_avg,Y_avg,Z_avg,X_var,Y_var,Z_var,X_gvty,Y_gvty,Z_gvty,X_lnr,Y_lnr,Z_lnr,User 1,Left, , , , , , , , , , , , ,ak 2,Left, , , , , , , , , , , , ,ak 3,Left, , , , , , , , , , , , ,ak 4,Left, , , , , , , , , , , , ,ak 5,Left, , , , , , , , , , , , ,ak 1,Left, , , , , , , , , , , , ,bp 2,Left, , , , , , , , , , , , ,bp 3,Left, , , , , , , , , , , , ,bp 4,Left, , , , , , , , , , , , ,bp 5,Left, , , , , , , , , , , , ,bp 1,Right, , , , , , , , , , , , ,hn 2,Right, , , , , , , , , , , , ,hn 3,Right, , , , , , , , , , , , ,hn 4,Right, , , , , , , , , , , , ,hn 5,Right, , , , , , , , , , , , ,hn 1,Right, , , , , , , , , , , , ,mm 2,Right, , , , , , , , , , , , ,mm 3,Left, , , , , , , , , , , , ,mm 4,Left, , , , , , , , , , , , ,mm 5,Left, , , , , , , , , , , , ,mm 1,Left, , , , , , , , , , , , ,nk 2,Left, , , , , , , , , , , , ,nk 3,Left, , , , , , , , , , , , ,nk 4,Left, , , , , , , , , , , , ,nk 5,Left, , , , , , , , , , , , ,nk 1,Left, , , , , , , , , , , , ,rk 2,Left, , , , , , , , , , , , ,rk 3,Left, , , , , , , , , , , , ,rk 4,Left, , , , , , , , , , , , ,rk 5,Left, , , , , , , , , , , , ,rk 1,Left, , , , , , , , , , , , ,rm 2,Left, , , , , , , , , , , , ,rm 3,Left, , , , , , , , , , , , ,rm 4,Left, , , , , , , , , , , , ,rm
14 PACE UNIVERSITY; CAPSTONE PROJECT - BIOMETRIC AUTHENTICATION & ACCELEROMETER SENSOR 14 5,Left, , , , , , , , , , , , ,rm
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