Palmprint Recognition Feature Extraction Algorithm

Biometrics combines biology technology and information technology to exploit physical features in human body such as fingerprint, iris, face and palmprint or behavioral features such as gait and signature to identify a person so as to replace or enhance traditional personal identification methods. This new technology can be applied to homeland security, social welfare, financial and e-commerce. Biometric is becoming the highest potential technology in this era.Palmprint recognition exploits the effective information on our palm to separate different persons. Different palms have totally different palmprints. In other words, palmprint is unique. Therefore, it can be used for personal identification. Palmprint research started for fortune telling. In the ancient China, Chinese knew that palmprint had a lot of useful information. In fact, palmprint has been applied for forensic applications for a long time. In the 1990s, we started working on automatic palmprint recognition for personal identification. As a new member of biometric family, palmprint has drawn great attentions from many research teams from all round the world since it's rich information, uniqueness and stable features. As a result, various palmprint identification methods have been proposed.This dissertation concentrates on the research of the key technologies and algorithms in palmprint identification system. According to the detail research of other biometrics, and considering the characteristics of palmprints, we propose a series of efficient palmprint preprocessing, feature extraction algorithms. In detail, this dissertation concentrates on the research of the following key1. Discuss the meaning, history, developing and the state of biometric and palmprint recognition.2. Introducing the main parts of palmprint recognition system, proposing a novel method of obtaining the interesting area of palmprint, and discuss two kinds of feature extraction method (palmprint lines feature and palmprint energy feature) with twoexamples.3. Proposing two novel feature extraction methods: the first method is based on principle component analysis and fisher linear discriminate analysis; the second method is based on 2-dimensional principle component analysis. And the two feature extraction method has been simulated and compared with other methods. Results show that the two feature extraction methods are very good.