  The use of physical and behavioral characteristics for human identification
is known as biometrics. Among the many biometrics traits available, the
fingerprint is the most widely used. The fingerprint identification is based on
the impression patterns, as the pattern of ridges and minutiae, characteristics
of first and second levels respectively. The current identification systems use
these two levels of fingerprint features due to the low cost of the sensors.
However, due the recent advances in sensor technology, it is possible to use
third level features present within the ridges, such as the perspiration pores.
Recent studies have shown that the use of third-level features can increase
security and fraud protection in biometric systems, since they are difficult to
reproduce. In addition, recent researches have also focused on multibiometrics
recognition due to its many advantages. The goal of this work was to apply
fusion techniques for fingerprint recognition in order to combine minutiae,
ridges and pore-based methods and, thus, provide more robust biometrics
recognition systems. We evaluated isotropic-based and adaptive-based automatic
pore extraction methods and the fusion of pore-based method with the
identification methods based on minutiae and ridges. The experiments were
performed on the public database PolyU HRF and showed a reduction of
approximately 16% in the Equal Error Rate compared to the best results obtained
by the methods individually.
