Fingerprint’s Core Point Detection using Gradient Field Mask

Fingerprint recognition is a widely used biometric identification mechanism. In case of correlation based fingerprint recognition detection of a consistent registration point is a crucial issue; this point can be a core point of a fingerprint. Many techniques have been proposed but success rate is highly dependent on input used and accurate core point detection is still an open issue. Here we discuss a core point detection algorithm which is computationally simple and gives consistent detection of a core point. The proposed technique is based on orientation field of the fingerprint which is calculated using gradient based technique and optimized neighborhood averaging to generate a smoother orientation field, on which we operate a specially designed mask for detecting core point as the orientation field in the region of the core point is different than the other area. Though all fingerprints don’t possess core point still this algorithm is useful to detect high curvature regions. This algorithm is helpful in the development of correlation based Automatic Fingerprint Recognition System (AFIS).