Fast Recognition of Multi-view Faces with Feature Selection

Zhi-Gang Fan and Bao-Liang Lu
Departmart of Computer Science and Engineering
Shanghai Jiao Tong University, Shanghai 200030, China


We propose a discriminative feature selection method
utilizing support vector machines for the challenging task
of multi-view face recognition. According to the statisti-
cal relationship between the two tasks, feature selection and
multi-class classification, we integrate the two tasks into a
single consist ....etc

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give me seminar report for java server faces for my project. which has also conclusion & abstract information about java server faces. ....etc

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Face Recognition Using Laplacian faces
Abstract: The face recognition is a fairly controversial subject right now. A system such as this can recognize and track dangerous criminals and terrorists in a crowd, but some contend that it is an extreme invasion of privacy. The proponents of large-scale face recognition feel that it is a necessary evil to make our country safer. It could benefit the visually impaired and allow them to interact more easily with the environment. Also, a computer vision-based authentication system could be put in pl ....etc

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Face Recognition Using Laplacian faces

Abstract: The face recognition is a fairly controversial subject right now. A system such as this can recognize and track dangerous criminals and terrorists in a crowd, but some contend that it is an extreme invasion of privacy. The proponents of large-scale face recognition feel that it is a necessary evil to make our country safer. It could benefit the visually impaired and allow them to interact more easily with the environment. Also, a computer vision-based authentication system could be put in ....etc

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java server faces seminar report and ppt

java server faces seminar report and ppt ....etc

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Principal Component Analysis
• PCA is a powerful tool for analyzing data.
• It is a way of identifying patterns in data, and expressing the data in such a way as to highlight their similarities and differences.
• Principal component analysis (PCA) involves a mathematical procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components.
• Principal Components Analysis is a method that reduces data dimensionality by performing a c ....etc

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Abstract—Dimensionality reduction is an important step in knowledge discovery in databases. Intrinsic dimension indicates the number of variables necessary to describe a data set. Two methods, box-counting dimension and correlation dimension, are commonly used for intrinsic dimension estimation. However, the robustness of these two methods has not been rigorously studied. This paper demonstrates that correlation dimension is more robust with respect to data sample size. In addition, instead of using a user selected distance , we propose a n ....etc

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Facial recognition using multisensor images based on localized kernel eigen spaces.

A feature selection technique along with an information fusion procedure for improving the recognition accuracy of a visual and thermal image-based facial recognition system is presented in this paper. A novel modular kernel eigenspaces approach is developed and implemented on the phase congruency feature maps extracted from the visual and thermal images individually. Smaller sub-regions from a predefined neighborhood within the phase congruency images of the ....etc

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