Multi-Algorithm Fusion for Fingerprint Recognition Based on Texture Features

Establishing the identity of a person with highconfidence using biometric systems are gaining importance. It is achallenge to improve the recognition rate of an existing unimodalbiometric system. Fingerprint recognition is one of the most matureand proven technology because of its immutability and individuality.Recognition result of the system is based mainly on feature extractionmethod and type of matcher used. This paper proposes amulti-algorithm fusion algorithm for fingerprint recognition. The mainobjective of the proposed system is to improve performance usingtexture features. Feature extraction is based on ridge information andtextures. Orientation features, Curvelet transform features and DualTree-Complex Wavelet Transform (DT-CWT) features are extractedand using Euclidean distance match scores are evaluated. Texturefeatures need very less pre-processing compared to orientationfeatures. With this speed of the recognition system is improved.Weighted sum method is used in fusion of matchers. Performance ofindividual matchers in terms of False Acceptance Rate (FAR) andFalse Reject Rate (FRR) has been evaluated. For optimal threshold(η),percentage genuine recognition rate (%GAR) is calculated. Algorithm is tested on fingerprint database of 100 users and also with FVC2002-DB3 database. Maximum recognition rate of 95.2% is achieved by combining Curvelet and DT-CWT features.