Handwriting Biometrics: Feature Selection Based Improvements in Authentication and Hash Generation Accuracy

Abstract

Biometric cryptosystems extend the user authentication functionality of usual biometric systems with the ability to generate robust stable values (also called biometric hashes) from variable biometric data. This work addresses a biometric hash algorithm applied to handwriting data and investigates the performance of both user authentication and hash generation scenarios. In order to improve the hash generation performance, some feature selection approaches are proposed. The intelligent reduction of features leads not only to a better ratio of collision/reproduction rates, but also improves equal error rates in user authentication scenario. Additionally, the parameterization of biometric hash algorithm is discussed. It has been shown that different quantization parameters as well as different features should be selected to achieve better performance rates in both scenarios. For the best semantic, symbol, the EER is improved from 8.30% to 5.27% and the CRR from 11.20% to 6.32%. Finally, the almost useful and needless features are figured out e.g. only 2 features are selected for every semantic in both scenarios and 10 features are never selected.