Fingerprint Recognition System Based on Mapping Approach

Abstract

Fingerprint identification system is mainly consisted of fingerprint achieving, fingerprint classification and fingerprint matching. Fingerprint matching is the key to the system and effects on the precision and efficiency of the whole system directly. Fingerprints are matched mainly based on their fingerprint texture pattern which can be described with the orientation field of fingerprints. A fingerprint, which has the different orientation angle structure in different local area of the fingerprint and has a texture pattern correlation among the neighboring local areas of the fingerprint, can be viewed as a Markov stochastic field. A novel method of fingerprint matching, which is based on embedded Hidden Markov Model (HMM) that is used for modeling the fingerprint's orientation field, is described in this paper. The accurate and robust fingerprint matching can be achieved by matching embedded Hidden Markov Model parameters which were built after the processing of extracting features from a fingerprint, forming the samples of observation vectors and training the embedded Hidden Markov Model parameters.

An Advanced Door Lock Security System using Palmtop Recognition System

“…Some works have also taken place on a fingerprint recognition system in an efficient way. Ways and methods for recognizing the fingerprint with the help of different methodologies are also available nowadays [70][71][72][73][74][75]. However, all of these approaches deal with only one fingerprint.…”

An Efficient Reconfigurable Architecture for Fingerprint Recognition

“…In the second stage, ridges inside HRC region were considered for matching. The global distribution structure and the local matching similarities [25] between fingerprints were considered for matching using Hidden Markov Model (HMM) [26]. Nikam and Agarwal [27] proposed spoof fingerprint detection using ridge let transform.…”