  This paper presents a novel approach for deciding on the appropriateness or
not of an acquired fingerprint image into a given database. The process begins
with the assembly of a training base in an image space constructed by combining
Principal Component Analysis (PCA) and edge detection. Then, the parameter H, a
new feature that helps in the decision making about the relevance of a
fingerprint image in databases, is derived from a relationship between
Euclidean and Mahalanobian distances. This procedure ends with the lifting of
the curve of the Receiver Operating Characteristic (ROC), where the thresholds
defined on the parameter H are chosen according to the acceptable rates of
false positives and false negatives.
