| In this thesis we present a new biometric authentication system using finger-knuckle-print (FKP) imaging. FKP refers to the inherent skin patterns of the outer surface around the phalangeal joint of one's finger.;Our FKP recognition system comprises four components: FKP image acquisition, ROI (region of interest) extraction, feature extraction, and feature matching. At first, an FKP image acquisition device is established. It is composed of a finger bracket, a ring LED light source, a lens, and a CCD camera. With the developed acquisition device, a large FKP database containing is established.;After an FKP image is captured, a region of interest (ROI) needs to be cropped from the original image for the further feature extraction and matching. Such a pre-processing step can reduce the data amount in the feature extraction and matching stage and can abate the influence of variations of the HUN. To this end, an efficient FKP ROI extraction algorithm is proposed based on the intrinsic characteristics of FKP images.;For feature extraction and matching, we have developed a couple of different methods. At first, the performances of several state-of-the-art coding based methods are evaluated. After that, a novel coding-based feature extraction and matching method, namely ImCompCode&MagCode is proposed, which is an extended version of CompCode. Moreover, we propose two more efficient and compact coding based feature extraction and matching approaches, RCode1 and RCode2, using Riesz transforms.;In fact, coding based feature extraction and matching methods make use of local information of images. Actually, global information hidden in images can also be exploited for recognition. Based on this belief, we have developed another FKP matching method by matching the Fourier transform coefficients of two FKP images using the phase-only correlation technique.;Furthermore, based on the results of psychophysics and neurophysiology studies that both local and global information is crucial for the image perception, we present an effective FKP recognition scheme by extracting and assembling local and global features of FKP images.;Based on the developed technologies, we implement a standalone embedded FKP recognition system, which can he readily used in practice. |
