Abstract
An error-correcting code (ECC) is a process of adding redundant data to a message, such that it can be recovered by a receiver even if a number of errors are introduced in transmission. Inspired by the principles of ECC, we introduce a method capable of detecting degraded features in biometric signatures by exploiting feature correlation. The main novelty is that, unlike existing biometric cryptosystems, the proposed method works directly on the biometric signature. Our approach performs a redundancy analysis of non-degraded data to build an undirected graphical model (Markov Random Field), whose energy minimization determines the sequence of degraded components of the biometric sample. Experiments carried out in different biometric traits ascertain the improvements attained when disregarding degraded features during the matching phase. Also, we stress that the proposed method is general enough to work in different classification methods, such as CNNs.