25-08-2017, 09:32 PM
26-08-2017, 03:57 PM
Touch fingerprint recognition is considered a viable alternative to contact-based fingerprint recognition technology. It provides an almost ideal solution to problems in terms of hygiene, maintenance and latent fingerprints. In this article we present a non-contact fingerprint recognition system using a digital camera. The system comprises stages of pre-processing, extraction of characteristics and adaptation. The proposed preprocessing stage shows promising results in terms of segmentation, enhancement and detection of central points. The extraction of the property is done by the Gabor filter and the favorable verification results are achieved with the support vector machine.
Non-contact fingerprint detection technologies have been developed to solve problems in contact detection techniques because they do not require any contact between a sensor and a finger. While they can solve problems caused by the contact of a finger. In order to overcome these difficulties, a new contactless fingerprint sensor is proposed that captures the image of the fingerprint with the camera. Human footprints are unique. A critical step in automatically matching fingerprints is to reliably extract minutiae from incoming fingerprint images. Here we first used several preprocessing steps in the binary image to improve image quality, followed by minutiae detection, removal of false minutiae and feature extraction. The final step is coincidence of minutiae. The proposed method can be applied to other biometric applications that require a large template for recognition.
Non-contact fingerprint detection technologies have been developed to solve problems in contact detection techniques because they do not require any contact between a sensor and a finger. While they can solve problems caused by the contact of a finger. In order to overcome these difficulties, a new contactless fingerprint sensor is proposed that captures the image of the fingerprint with the camera. Human footprints are unique. A critical step in automatically matching fingerprints is to reliably extract minutiae from incoming fingerprint images. Here we first used several preprocessing steps in the binary image to improve image quality, followed by minutiae detection, removal of false minutiae and feature extraction. The final step is coincidence of minutiae. The proposed method can be applied to other biometric applications that require a large template for recognition.