A feature extraction method using the chaincode representation of fingerprint ridge contours is presented for use by Automatic Fingerprint Identification Systems. The representation allows efficient image quality enhancement and detection of fine feature points called minutiae. Enhancement is accomplished by binarization and smoothing followed by estimation of the ridge contours field of flow. ...

The Venus of Dolní Věstonice I (Gravettian, 25, 000 B.P.) was discovered on July 13th, 1925 in Dolní Věstonice, South Moravia (Czechoslovakia), during Moravian Museum excavations. The figurine, made from fired clay, about 11.5 cm high, represents a woman with a plump figure. More than 75 years after its discovery, a fingerprint on the left side of the figurine back was analyzed. The dimensions ...

A procedure for constructing a vector of regression weights is considered. Under the regression superpopulation model, the ridge regression estimator that has minimum model mean squared error is derived. Through a simulation study, the ridge regression weights, regression weights, quadratic programming weights and raking ratio weights are compared. The ridge regression procedure with weights bo...

Computing a reliable orientation map is a critical step in automatic fingerprint analysis and especially for analysis of fingermarks obtained at crime-scenes especially. Being the initial step of processing image information it may influence the further operations: registration, enhancement and matching. We suggest a new way of automatic frequency estimation improving the state-of-theart result...

In this paper, fingerprint image is mathematically modeled by using a 2D sinusoidal function in a local window of size 32x32. The estimated ridge distance is then found by using the Levenberg-Marquardt gradient descent method. From test images, it has been found that the error percentage is 5% or less for fingerprint images of good to moderate quality with ridge distances between five and 20 pi...

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://links.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive...

Feedforward neural networks, projection pursuit regression, and more generally, estimation via ridge functions have been proposed as an approach to bypass the curse of dimensionality and are now becoming widely applied to approximation or prediction in applied sciences. To address problems inherent to these methods – ranging from the construction of neural networks to their efficiency and capab...

This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related nonlinearly to the target variable. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation of the predictive regression model is based on a shrinkage estimator to avoid overfitting. We extend the kernel ...

An obvious Bayesian nonparametric generalization of ridge regression assumes that coefficients are exchangeable, from a prior distribution of unknown form, which is given a Dirichlet process prior with a normal base measure. The purpose of this paper is to explore predictive performance of this generalization, which does not seem to have received any detailed attention, despite related applicat...