Automatic fingerprint matching based on an innovative ergodic embedded hidden markov model (E2HMM) approach

Matching is an important step in any fingerprint recognition system. In this paper, a fingerprint matching technique based on hidden markov model is proposed. This method uses only the ridge orientation information around the reference point of registered fingerprint image. At the first step, fingerprint images are aligned according to a suitable reference point. Then in the second step, the ridge orientation field around this point is applied to the HMM based on an innovative topology. This topology provides many significant advantages such as simplicity, flexibility and generality. The selected orientation field forms specific feature vectors so that can be used for proposed HMM matching process. In the HMM matching process, the maximum likelihood between training and test feature vectors are tested according to a predefined threshold. For evaluating the proposed matching method, an artificial continuous classification had been applied over FVC2000 DB2_A. The results of experiment have proved the higher efficiency and robustness of the proposed method in comparison with the competing one.