Research On Handmetric Recognition And Feature Level Fusion Method

Biometrics is the most secure and convenient way to satisfy the requirements for identity digitalization and virtualization in the coming network society, which refers to the automatic identification of an individual by using certain physiological or behavioral traits associated with the person. Multimodal biometric system is more robust than unimodal one. It takes advantages of multiple biometric traits to improve the performance in many aspects including accuracy, noise resistance, universality, spoof attacks, and reduce performance degradation in huge database applications. Recently, new algorithms and applications of multimodal biometrics are emerging rapidly. The research of building low cost, low database complexity, low intrusiveness system while developing different fusion strategy, algorithm in different fusion level is a promising new field and becoming the focus of recent studies.The goal of the thesis is to implement such a system by handmetric recognition based on feature level fusion strategy. The proposed "handmetric" refers to the fusion of palmprint, knuckleprint and hand geometry. First, we present several algorithms for palmprint and knuckleprint recognition respectively. Then, based on the investigation of the existing feature level fusion algorithms and the idea of well studied decision score level fusion method, a strategy for feature level fusion named "Relation Measurement Fusion (RMF)" is proposed. The final handmetric recognition system is implemented by RMF, and the evaluation results validate the effectiveness of it. Main content of this work is as follows:1. The definition of handmetric is presented. The use of handmetric accords with the traditional conception of "identity". While in the field of multimodal biometrics, handmetric system is designed to solve the problem of inconvenience, cost and database management by acquiring different evidences in the same hand image. An image database called "HA-BJTU" for further study is constructed using a CCD camera. From the images, we could obtain palmprint, knuckleprint and hand geometry...