1 T.Revathy, 2 Ms.G.Pramila, 3 Dr.A.Askarunisa, 4
Mr.A.Athiraja
1 PG Student, 2 Assistant Professor, 3 Professor & Head, 4 Assistant Professor
Department of Computer Science and engineering, Vickram College Of Engineering, Enathi, Sivagangai, India.
1 PG Student, 2 Assistant Professor, 3 Professor & Head, 4 Assistant Professor
Department of Computer Science and engineering, Vickram College Of Engineering, Enathi, Sivagangai, India.
Latent fingerprints are
lifted from crime scenes in a routine procedure that is extremely important to
forensics and law enforcement agencies. Since it contains as small regions of
fingerprint, latents have a significantly smaller number of minutiae points
compared to full (rolled or plain) fingerprints. The small number of minutiae
and the noise characteristic of latents make it extremely difficult to
automatically match latents to their mated full prints that are stored in law
enforcement databases. To overcome this problem, the automatic segmentation is
used. An important step in an automatic fingerprint recognition system is the
process of automatically segmenting the fingerprint images. In this paper, the
segmentation process consists of the three blocks namely (i) segmentation of
Orientation feature, (ii) Segmentation of Frequency feature and (iii) Post
processing. The Orientation Feature process is used to obtain the fingerprint
ridge orientations, the Frequency Feature process is used to obtain the local
ridge frequencies of the fingerprint. The Post Processing method combine these
two results to find the candidate fingerprint regions (foreground).The proposed
scheme achieves accurate segmentation for latent matching of fingerprint. This
system improves the accurate segmentation of foreground and background regions
from latent fingerprint images. This system gives 77% of accuracy to detect a
foreground region from latent fingerprint images.
