Publication

Face Class Modeling in Eigenfaces Space

Jean-Philippe Thiran, Vlad Popovici
2003
Conference Papers

Abstract

We present a method for face class modeling in the eigenfaces space using a large-margin classifier like SVM. Another issue addressed is how to select the number of eigenfaces to achieve a good classification rate. As the experimental evidence show, generally one needs less eigenfaces than usually considered. We will present different strategies and discuss their effectiveness in the case of face-class modeling.

Official source

https://infoscience.epfl.ch/entities/publication/0b393bb2-4b1a-411f-9aa4-f99341120c15

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Jean-Philippe Thiran, Vlad Popovici
2003
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/0b393bb2-4b1a-411f-9aa4-f99341120c15

About this result

Related concepts (32)

Eigenface

An eigenface (ˈaɪgənˌfeɪs) is the name given to a set of eigenvectors when used in the computer vision problem of human face recognition. The approach of using eigenfaces for recognition was developed by Sirovich and Kirby and used by Matthew Turk and Alex Pentland in face classification. The eigenvectors are derived from the covariance matrix of the probability distribution over the high-dimensional vector space of face images. The eigenfaces themselves form a basis set of all images used to construct the covariance matrix.

Support vector machine

In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Cortes and Vapnik, 1995, Vapnik et al., 1997) SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974).

Linear classifier

In the field of machine learning, the goal of statistical classification is to use an object's characteristics to identify which class (or group) it belongs to. A linear classifier achieves this by making a classification decision based on the value of a linear combination of the characteristics. An object's characteristics are also known as feature values and are typically presented to the machine in a vector called a feature vector.

Related publications (29)

Face Class Modeling in Eigenfaces Space

Chat with Graph Search

Nanoelectronic devices comprising patterned thin films and method for producing the same

Nanoelectronic devices comprising patterned thin films and method for producing the same

On the Generation of Face Morphs by Inversion of Optimal Morph Embeddings