Abstract—We present an example-based learning approach for locating vertical frontal views of human faces in complex scenes. The technique models the distribution of human face patterns by means of a few view-based “face ” and “nonface ” model clusters. At each image location, a difference feature

The growing interest in Structured Equation Modeling (SEM) techniques and recognition of their importance in IS research suggests the need to compare and contrast different types of SEM techniques so that research designs can be appropriately selected. After assessing the extent to which

Palmprint has proved to be one of the most unique and stable biometric characteristics. Almost all the current palmprint recognition techniques capture the 2-D image of the palm surface and use it for feature extraction and matching. Although 2-D palmprint recognition can achieve high accuracy

. Classification is done on basis of correlation between training and testing set images. The approach is tested over a data set of 200 images divided into 10 classes and seen to provide 100% recognition accuracy. A contemporary technique is also implemented on the current dataset for comparison of accuracy

learning theory, neural networks, statistics, and pattern recognition have discovered one another and begun to work together. Second, machine learning techniques are being applied to new kinds of problems including knowledge discovery in databases, language processing, robot control, and combinatorial

optimized using designations provided by the crystallog-raphers as a standard-of-truth. Comparison to the currently most widely used technique DSSP by Kabsch and Sander (Biopolymers 222577-2637, 1983) shows that STRIDE and DSSP as-sign secondary structural states in 58 and 31% of 226 protein chains in our

) is extracted. Then the Competitive Coding (CompCode) technique is used to code the 3D palmprint pattern according the MCI. By using score level fusion of MCI and its CompCode, promising recognition performance is achieved on our established 3D palmprint database.

techniques for palmprint authentication fall considerably, when the camera is not aligned with the surface of the palm. The problems arise primarily due to variations in appearance introduced due to varying pose, but is compounded by specularity of the skin and blur due to motion and focus. In this paper, we

Abstract: Biometric recognition refers to an automatic recognition of individuals based on a feature vector(s) derived from their physiological and/or behavioral characteristic. Palmprint recognition is one of the popular methods which has been investigated over last fifteen years due to its

based on the principal lines, wrinkles and ridges on the surface of the palm. The recognition process consists of image acquisition, preprocessing, feature extraction, matching and result. The different techniques are used for the preprocessing, feature extraction, classifiers. The methods discussed