Extended Feature Set in Fingerprint Matching

Yi Chen


There are fundamental differences in the way fingerprints are compared by forensic experts and current Automatic Fingerprint Identification Systems (AFIS). For example, while AFIS focus mainly on the quantitative measures of fingerprint minutiae (ridge ending and bifurcation points), latent experts often analyze details of intrinsic ridge characteristics and relational information. The process of qualitative friction ridge analysis includes examination of minutiae shape, dots, incipient ridges, local ridge quality, and ridge tracing, This explains the challenges that current AFIS face in processing poor quality prints, especially latent prints. In fact, most of the features used by latent experts have not even been quantitatively defined for AFIS matching. The forensics as well as the AFIS communities have become very active in standardizing the definition of extended feature set, as well as quantifying the relevance and reliability of these features for automatic systems. CDEFFS (Committee to Define an Extended Feature Set) has proposed a draft on possible definitions and representations of extended features. The FBI Lab is also completing a study on the permanence of various friction ridge characteristics including these extended features. This lecture will describe the foundamental characteristics of extended feature set. We will introduce and discuss previous work on utilizing them in fingerprint matching as well as algorithms that have been recently developed to automatically extract extended features. A systematic framework that automatically utilize multi-level fingerprint features will be demonstrated at last.



European Commission

EU Horizon 2020



Technical Committee on Biometrics (TC4)



European Association for Signal Processing



Morpho - Safran group


EAB European Association for Biometrics


Biometrics Institute




University of Sassari