README NOTES:
The project is in python 3.7. So, to run a program you will be need a python environment and also need to import packages pandas and Scipy. I have implemented this program in Python IDE development environment. Please change the path of the input data in the program.
N=200 there is a file name 200 where you can change the threshold value in Ôthr=' attribute and can get output for different threshold values. Same goes for N=100(100) and N=300(300).
You are required to implement Manhattan verifier and report false accept (impostor pass) and false reject rates on a publicly available keystroke biometric dataset. You may use any programming language, as long as it can be compiled.
Dataset: The data consist of keystroke-timing information from 51 subjects (typists), each typing a password (.tie5Roanl) 400 times. (http://www.cs.cmu.edu/~keystroke/)
Verification Task: For each user, (a) compute the template using mean key hold and key interval features calculated on the first N typing samples; (b) compute the genuine and impostor scores using Manhattan distance; and (c) calculate and report false accept (im- postor pass) and false reject rates at a given threshold T.
Program Input: (1) N is the number of samples to be used for building the template (e.g., if N = 200, use the first 200 samples of each user to compute the average vector and the remaining 200 for testing; if N = 100, use the first 100 samples for the template and the remaining 300 for testing); and (2) T is the verification threshold.
Program Output: Clearly display false accept (impostor pass) and false reject rates at a given threshold T.