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Touch-less Fingerprint Recognition Using SVM and GMM: A Comparative Study

Shweta Warade, Rajesh Patil

Fingerprint biometrics provides identity verification with a strong degree of confidence over past few decades. Present work focuses on special branch of fingerprint recognition namely, touch-less fingerprint recognition which has fundamental advantage in terms of better hygiene, safety and latent fingerprints over traditional touch-based systems. Such systems find applications in numerous fields such as secure access to laptops and computer systems, cellular phones, banking, ATMs etc. Although lot of work have been done on touch-less fingerprint recognition systems, still people are looking for more accuracy. The proposed touch-less fingerprint recognition system opt digital camera as the device to acquire the fingerprint image and it consists of three main steps: Pre-processing, Feature extraction and Verification. Current work presents a comparative performance evaluation of two widely used classifiers: Support Vector Machine (SVM) and Gaussian Mixture Model (GMM). Experimental results illustrates that GMM shows slightly better accuracy than SVM.