palmprint recognition is a new biometrics system based on physiological characteristics of the palmprint, which includes rich, stable, and unique features such as lines, points, and texture. texture is one of the most important features extracted from low resolution images. in this paper, a new local descriptor, local composition derivative pattern (lcdp) is proposed to extract smartly stronger...

A class of higher-order networks called Pi-Sigma networks has recently been introduced for function approximation and classiication 4]. These networks combine the fast training abilities of single-layered feedforward networks with the non-linear mapping of higher-order networks, while using much fewer number of units. In this paper, we investigate the applicability of these networks for shift, ...

Cluster structure in (multicollinear) data can be utilized by pattern recognition methods in order to find adequate subspaces for nonlinear regression. When regressing a particular severely nonlinear function, it is demonstrated that this approach is superior to polynomial PLS. It is also demonstrated that for nonlinear functions, the choice of regressing explained variables onto the explaining...

Texture is one of the primary visual attributes used to describe patterns found in nature. Several texture analysis methods have been used as powerful tools for real applications involving computer vision. However, existing methods do not successfully discriminate the complexity of texture patterns. Such methods disregard the possibility of describing image structures by fractal dimension. Frac...

Probability density function estimation from limited data sets is a classical problem in pattern recognition. In this paper we propose a reformulation of the well-known nonparametric Parzen method as a parametrically regularized Gaussian Mixture Model, from which we can easily estimate density contour level. As an application illustration to the proposed contour level estimator, we also address...

\Scale-space" is an important recent concept used in image and signal processing and pattern recognition. Traditional scale-space is generated by a linear Gaus-sian smoothing operation. We present here a nonlinear type of smoother corresponding to the multiscale opening and closing operations of mathematical morphology which also generates a \scale-space." We show that a parabolic structuring e...

Studies by various authors suggest that higher-order networks can be more powerful and are biologically more plausible with respect to the more traditional multilayer networks. These architectures make explicit use of nonlinear interactions between input variables in the form of higher-order units or product units. If it is known a priori that the problem to be implemented possesses a given set...

I discuss the construction of models that describe the ring rates of excitatory and inhibitory neurons in biological neural networks. A model is presented that incorporates both slow linear and fast nonlinear inhibition. With the appropriate excitatory-to-excitatory couplings this model can act as an associative memory in which pattern recognition is signalled by resonant ring behavior. Stored ...

In this paper the problem of nonlinear feature extraction based on the optimization of the Fisher criterion is analyzed. A new nonlinear feature extraction method is proposed. The 15 method does not make use of numerical algorithms and it has an analytical (closed-form) solution. Moreover, no assumptions on the class probability distribution functions are 17 imposed. The proposed method is appl...

This paper examines the degrees of correlation among vocal-tract and facial movement data and the speech acoustics. Multilinear techniques are applied to support the claims that facial motion during speech is largely a byproduct of producing the speech acoustics and further that the spectral envelope of the speech acoustics can be better estimated by the 3D motion of the face than by the midsag...