Palm line extraction and matching for personal authentication

The palm print is a new and emerging biometric feature for personal recognition. The stable line features or "palm lines", which are comprised of principal lines and wrinkles, can be used to clearly describe a palm print and can be extracted in low-resolution images. This paper presents a novel approach to palm line extraction and matching for use in personal authentication. To extract palm lines, a set of directional line detectors is devised, and then these detectors are used to extract these lines in different directions. To avoid losing the details of the palm line structure, these irregular lines are represented using their chain code. To match palm lines, a matching score is defined between two palm prints according to the points of their palm lines. The experimental results show that the proposed approach can effectively discriminate between palm prints even when the palm prints are dirty. The storage and speed of the proposed approach can satisfy the requirements of a real-time biometric system

I. Introduction

Computer-Aided personal recognition is becoming increasingly important in our information-based society, and within this field biometrics is one of the most important and reliable methods [1], [2]. The most widely used biometric feature is the fingerprint [3], [4] and the most reliable feature is the iris [1], [5], [6]. However, it is very difficult to extract small unique features (known as minutiae) from unclear fingerprints [3], [4] and iris input devices are very expensive. Other biometric features, such as the face [7], [8] and the voice [9], [10], are as yet not sufficiently accurate. Compared with all of these, the palm print, a relatively new biometric feature, has several advantages [11]. Palm prints contain more information than fingerprints, so they are more distinctive. Palm print capture devices are much cheaper than iris devices. Further, palm prints contain additional distinctive features such as principal lines and wrinkles, which can be extracted from low-resolution images. By combining all the features of palms, such as palm geometry, ridge and valley features, and principal lines and wrinkles, it is possible to build a highly accurate biometrics system.