Tangent Distance (TD) is one classical method for invariant pattern classification. However, conventional TD need pre-obtain tangent vectors, which is difficult except for image objects. This paper extends TD to more general pattern classification tasks. The basic assumption is that tangent vectors can be approximately represented by the pattern variations. We propose three probabilistic subspace models to encode the variations: the linear subspace, nonlinear subspace, and manifold subspace models. These three models are addressed in a unified view, namely Probabilistic Tangent Subspace (PTS). Experiments show that PTS can achieve promising classification performance in non-image data sets.

Automic palmprint verification is an important complement of biometric authentication. As the first attempt of personal identification by palmprint, this paper explores different methods for three main processing stages in palmprint verification including datum point registration, line feature extraction and palmprint classification. The datum points of palmprint which have the remarkable advantage of invariable location are defined and their determination method using the directional projection algorithm is improved. Then, line feature extraction and line matching method is described to detect whether a couple of palmprints are from the same palm. In addition, palmprint classification method based on the orientation property of the ridges is discussed to distinguish six typical cases. Various palmprint images have been tested to illustrate the effectiveness of the proposed methods.

True minutiae extraction in fingerprint image is critical to the performance of an automated identification system. Generally, a set of endings and bifurcations (both called feature points) can be obtained by the thinning image from which the true minutiae of the fingerprint are extracted by using the rules based on the structure of ridges. However, considering some false and true minutiae have similar ridge structures in the thinning image, in a lot of cases, we have to explore their difference in the binary image or the original gray image. In this paper, we first define the different types of feature points and analyze the properties of their ridge structures in both thinning and binary images for the purpose of distinguishing the true and false minutiae. Based on the knowledge of these properties, a fingerprint post-processing approach is developed to eliminate the false minutiae and at the same time improve the thinning image for further application. Many experiments are performed and the results have shown the great effectiveness of the approach.

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