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 on computers in HSH 212. In addition. I will ask you to demonstrate and explain your programs. 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 (impostor pass) and false reject rates at a given threshold T. Program Input: (I) 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. Deliverables: Well documented, compliable software codes and executables performing template calculation; genuine and impostor score computation with Manhattan distance; and calculation of false accept and false reject rates at a given threshold T. A well-written report containing false accept and false reject rates for N = 200 and various threshold values (choose five threshold values that give you the best tradeoff between the false accept and false reject rates). Report the false accept rate at 0 false reject rate, when N= 100, 200, and 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 on computers in HSH 212. In addition. I will ask you to demonstrate and explain your programs. 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 (impostor pass) and false reject rates at a given threshold T. Program Input: (I) 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. Deliverables: Well documented, compliable software codes and executables performing template calculation; genuine and impostor score computation with Manhattan distance; and calculation of false accept and false reject rates at a given threshold T. A well-written report containing false accept and false reject rates for N = 200 and various threshold values (choose five threshold values that give you the best tradeoff between the false accept and false reject rates). Report the false accept rate at 0 false reject rate, when N= 100, 200, and 300