Towards robust biohash generation for dynamic handwriting using feature selection

Biometric hashing has the objective to robustly generate stable values from variable biometric data of each particular user and at the same time to generate different values for different users. The quality of hash generation is therefore determined by reproduction and collision rates, which represent the probabilities of hash reproduction in genuine and impostor trials correspondingly. In our work, hash vectors are created based on statistical feature set extracted from dynamic handwritten data. Since the choice of features has been done rather intuitively, it can be observed, that some features have very high intra-class variance and cannot be reproduced for some users. Other features have very low inter-class variance and are always reproduced in impostor trials. Thus, feature selection is required to eliminate all irrelevant features and to allow reliable hash generation. This work compares several feature selection strategies on different writing contents and proves their effectiveness in experimental evaluation. Our experiments show that the best feature selection strategy improves reproduction/collision rates, at an average, to approx. 40%. This makes the robust biometric hash generation with reproduction rate of 93.40% and collision rate of 6.67% practical.