In this paper, a novel method for palmprint recognition, called Fisherpalms, is proposed. In this method, each pixel of a palmprint image is considered as a coordinate in a high-dimensional image space. A linear projection based on Fisher s linear discriminant is used to project palmprints from this high-dimensional original palmprint space to a significantly lower dimensional feature space (Fisherpalm space), in which the palmprints from the different palms can be discriminated much more efficiently. The relationship between the recognition accuracy and the resolution of the palmprint image is also investigated. The experimental results show that, in the proposed method, the palmprint images with resolution 32-32 are optimal for medium security biometric systems while those with resolution 64 64 are optimal for high security biometric systems. High accuracies (>99%) have been obtained by the proposed method and the speed of this method (responding time≤0.4s) is rapid enough for real-time palmprint recognition.

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 theproposed approach can satisfy the requirements of a real-timebiometric system.

This paper presents a novel approach of palmprint authentication based on the fusion of the phase and orientation information. This approach is an improvement of a previous palmprint recognition method-FusionCode method [1]. In the proposed approach, the phase information (Fusion Code) of a palmprint is extracted by using four 2-D Gabor filters with different orientations, and at the same time, the orientation information (called OrientationCode) of the palmprint is also extracted. The FusionCode and the OrientationCode are fused to make a new feature, called the Palmprint Phase Orientation Code (PPOC). At the matching stage, a modified Hamming distance is defined to measure the similarity of two PPOCs. This approach is tested on a palmprint database containing 7605 samples and the experimental results show that the PPOC approach greatly improves the performance of the FusionCode method.

According to the fact that the basic features of a palmprint, including principal lines, wrinkles and ridges, have different resolutions, in this paper we analyze palmprints using a multi-resolution method and define a novel palmprint feature, which called wavelet energy feature (WEF), based on the wavelet transform. WEF can reflect the wavelet energy distribution of the principal lines, wrinkles and ridges in different directions at different resolutions (scales), thus it can efficiently characterize palmprints. This paper also analyses the discriminabilities of each level WEF and, according to these discriminabilities, chooses a suitable weight for each level to compute the weighted city block distance for recognition. The experimental results show that the order of the discriminabilities of each level WEF, from strong to weak, is the 4th, 3rd, 5th, 2nd and 1st level. It also shows that WEF is robust to some extent in rotation and translation of the images. Accuracies of 99.24% and 99.45% bave been obtained in palmprint verification and palmprint identification, respectively. These results demonstrate the power of the proposed approach.

作为一种较新的生物特征，掌纹可用来进行人的身份识别。在用于身份识别的诸多特征中，掌纹线，包括主线和皱褶，是最重要的特征之一。本文为掌纹识别提出一种有效的掌纹线特征的表示和匹配方法。该方法定义了一个矢量来表示一个掌纹上的线特征，该矢量称为线特征矢量（line feature vector，简称LFV）。线特征矢量是用掌纹线上