A fingerprint identification system is popular and important in biometric identification applications. Its process is composed of two stages: feature extraction and matching. Most fingerprint features include ending points and bifurcation called minutiaes. In this paper, we propose a new fingerprint binarization method based on convex threshold for effective minutiaes extraction. The proposed m...

We consider the problem of model selection and estimation in sparse high dimensional linear regression models with strongly correlated variables. First, we study the theoretical properties of the dual Lasso solution, and we show that joint consideration of the Lasso primal and its dual solutions are useful for selecting correlated active variables. Second, we argue that correlation among active...

Correctly estimating fingerprint ridge orientation is an important task in fingerprint image processing. A successful orientation estimation algorithm can drastically improve the performance of tasks such as fingerprint enhancement, classification, and singular points extraction. Gradient-based orientation estimation algorithms are widely adopted in academic literature, but they cannot guarante...

Minutiae are the two most prominent and well-accepted classes of fingerprint features arising from local ridge discontinuities: ridge endings and ridge bifurcations. However, the preprocessing stage doesn’t eliminate all possible defects in the original gray-level image and the orientation estimation in a poor image is extremely unreliable. In order to further eliminate the false minutiae cause...

This paper studies the retinal vessel radius estimation and proposes a segmentation method for vessel center lines based on ridge descriptors. The study on radius estimation reveals that the radius estimation by the matched filters based on the second order derivatives of Gaussian kernels is only correct at the vessel center. The relation between the vessel radius and the scale of the Gaussian ...

A common problem in multiple regression models is multicollinearity, which produces undesirable effects on the least squares estimator. To circumvent this problem, two well known estimation procedures are often suggested in the literature. They are Generalized Ridge Regression (GRR) estimation suggested by Hoerl and Kennard [8] and the Jackknifed Ridge Regression (JRR) estimation suggested by S...