A novel fingerprint matcher based on an ergodic 2-D Hidden Markov Model

In this paper, a new approach for fingerprint ridge orientation field matching based on a novel HMM (Hidden Markov Model) is proposed. The proposed method comprises several steps. First steps are devoted to regular fingerprint preprocesses and ridge orientation estimation. Then, the fingerprint images are registered along a reference point. Next, the proposed HMM topology is applied to the predetermined fingerprint orientation field information around the reference point. The suggested HMM is of improved training abilities. After applying the proposed HMM to the ridge orientation field, the matching cells are produced. These cells consist of transition, observation and initial probability matrices which will be used in the matching procedure. The proposed matching method has been evaluated using some creditable fingerprint databases such as FVC2000 DB2_A, FVC2004 DB3_A and DB4_A. The evaluation results confirm higher efficiency, robustness and accuracy for the proposed method compared with the previously proposed matching ones.