The dichotomy model is a powerful, but little-explored, technique for biometric authentication (verification). A comparison of this technique to other authentication techniques could produce an outstanding dissertation in the area of biometrics, especially if it is supported by comparative experiments which could be performed on our extensive biometric (especially keystroke) databases. The dichotomy model was used in Dr. Cha's dissertation, see key paper for this model, and in an on-line fingerprint verification study. Also, see the subset of dichotomy slides from a conference paper.
Last semester a generic dichotomy-model authentication system was created. It accepts feature-vector data in the specified Feature Data Format and converts it into a two-class dichotomy-model authentication data file. The two authentication classes are the within-class (same person) and the between-class (different people) categories. The conversion is performed by taking all possible difference vectors, or by limiting the number of within-class/between-class samples (say, to 500/500 or 1000/1000).
In general, if n people provide m biometric samples each, there are m*(m-1)*n/2 within-class pairs and m*m*n*(n-1)/2 between-class pairs (see the key paper reference above). The number of between-class pairs usually far exceeds the number of within-class pairs. Sometimes both the number of within-class and between-class pairs can be large (possibly in the millions), and then the training and test samples can be generated at random and limited, rather than fully elaborated. For each pair, a difference vector is computed by taking the absolute difference between each vector component. Because our biometric features are in the range 0-1, the difference vector features will also be in the range 0-1.
After the dichotomy-model conversion, authentication system performance results can be obtained by using the available nearest-neighbor program to obtain accuracy results on the data (actually, these programs might be combined). This technique simply computes the Euclidean distance of each testing sample to all the training samples, and assigns the test sample to the class of the nearest training sample. A textbook (Guide to Biometrics, by Bolle, et al., Springer 2004, ISBN 0387400893) will be provided to the team (book must be returned at the end of the semester) that describes the performance statistics, namely False Accept Rate (FAR) and False Reject Rate (FRR), that should be obtained on the various biometric data sets.
Your first task is to understand the existing system by reading last semester's technical paper and communicating with your subject matter expert (team leader from last semester). Then rerun some of the experiments to ensure that you understand the system.
Find the answers to the following questions:
Midterm Checkpoint (our second classroom meeting).
By this checkpoint you should understand the existing system and have rerun some of the experiments from last semester to ensure that you understand the system. You might also correct any problems with the code as determined from the end-of-February checkpoint.