Dry fingerprint detection for multiple image resolutions using ridge features

Abstract

Dry and wet fingers lead to poor fingerprint quality, which means that it has impact for fingerprint recognition and matching. Recognition methods that are based on the feature of ridge, valley, minutiae or pore are affected by skin conditions. In this paper, we propose a novel dry fingerprint detection method for images with different resolutions using ridge features. The dry fingerprints have vague pores and discontinuous and fragmented ridges. Therefore, the features that we adopt for detection are ridge continuity, ridge fragmentation and ridge/valley ratio. These features can be observed clearly under different image resolutions, so our proposed method can work on 500∼1200 dpi. We propose several ridge features and use the support vector machine to classify into two groups, dry and normal. The NASIC database (1200dpi) and FVC2002 DB1 (500dpi) are used in our experiments, the SVM classification accuracy are 99.00%, and 99.09% relatively.