FPR using machine learning with multi-feature method

Biometrics authentication is considered as most secure and reliable method to recognise and identify person's identity. Researchers put efforts to find efficient ways to secure and classify the solutions to biometric problems. In this category, fingerprint recognition (FPR) is most widely used biometric trait for person identification/verification. The present work focuses an FPR technique, which uses the grey-level difference method, discrete wavelet transforms and edge histogram descriptor for fingerprint representation and matching. Wavelet shrinkage used for noise removal in the image. Ridge flow estimation is calculated using the gradient approach. SVM and Hamming distance similarity measures are used for recognition. The experiment result has been tested on the standard 2000–2004 fingerprint verification competition dataset and the accuracy of proposed algorithm was reported to be well above 98%.