FUSION BEFORE MATCHING BIOMETRICS

Fusion in this category integrates evidences before matching or comparison of data samples against the user sample occurs. According to Kokar et al., “By combining low level features it is possible to achieve a more abstract or a more precise representation of the world”. Thus, biometric sources at the earlier stage contain much more information than after processing.

However, the extra costs of storing raw data and additional complexity in developing matching methods do not make this approach quite practical.

Fusion after-matching methods consolidate information obtained after individual biometric matching or comparison is done. Most multimodal biometric systems have been using these fusion methods as the information needed for fusion is easily available compared to fusion before matching methods. The matching scores, the ranking list (sorted order) based on matching scores or the individual biometric decision (Yes/No) can be used for fusion in this category.

Information fusion techniques applied in multimodal biometrics area are discussed. Usually, the information originated from different sources in a multimodal biometric system can be combined in senor level, feature extraction level, match score level, rank level, and decision level. Among all of the fusion methods, senor fusion and feature extraction level fusion considered as the stage for combining raw data or the actual biometric data. Match score, rank and decision level fusion methods combine processed data or data obtained through some experimentations. There is also another novel fusion method which is becoming highly popular: the fuzzy fusion.