Biometrics and Bioinformatics

Malaya Dutta Borah ¹, Ganesh Chandra Deka ²

Affiliations
1 Delhi College of Engineering, Bawana Road, Delhi-42, IN
2 Govt of India, New Delhi-1, IN

Abstract

One of the most challenging Biometric Authentication is fingerprint identification. In 1893, the Home Ministry office, UK accepted that no two individuals have the same fingerprints, i.e. never identical in every detail. Due to this property fingerprints are widely used by law enforcement applications. Fingerprint identification system mostly consists of extraction, classification and matching. In this paper we are going to propose a theoretical model for fingerprint matching using Hidden Markov Model (HMM). Our main focus is on extracting reference information; detect sample vectors, pretreatment including noise illumination and training process of HMM engine. The underlying assumption of proposed HMM is that the fingerprint texture pattern and orientation can be well characterized as a parametric random process and hence reduce the size of the search space of a fingerprint database and accordingly increase the speed of fingerprint matching.

Keywords

Hidden Markov Model (HMM), Fingerprints Matching, Biometric Authentication, Parametric Random Process, Texture Pattern, Welch Re-Estimation.

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Malaya Dutta Borah ¹, Ganesh Chandra Deka ²

Affiliations
1 Delhi College of Engineering, Bawana Road, Delhi-42, IN
2 Ministry of Labour & Employment, Govt. of India, New Delhi-1, IN

Abstract

One of the most challenging Biometric Authentication is fingerprint identification. In 1893, the Home Ministry office, UK accepted that no two individuals have the same fingerprints, i.e. never identical in every detail. Due to this property fingerprints are widely used by law enforcement applications. Fingerprint identification system mostly consists of extraction, classification and matching. In this paper we are going to propose a theoretical model for fingerprint matching using Hidden Markov Model (HMM). Our main focus is on extracting reference information; detect sample vectors, pretreatment including noise illumination and training process of HMM engine. The underlying assumption of proposed HMM is that the fingerprint texture pattern and orientation can be well characterized as a parametric random process and hence reduce the size of the search space of a fingerprint database and accordingly increase the speed of fingerprint matching.

Keywords

Hidden Markov Model (HMM), Fingerprints Matching, Biometric Authentication, Parametric Random Process, Texture Pattern, Welch Re-Estimation.

Full Text