What is the meaning of Eigenface?

What is the meaning of Eigenface?

An eigenface (/ˈaɪɡənˌfeɪs/) 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.

What is the Eigenface approach?

Eigenfaces refers to an appearance-based approach to face recognition that seeks to capture the variation in a collection of face images and use this information to encode and compare images of individual faces in a holistic (as opposed to a parts-based or feature-based) manner.

How do you calculate eigenfaces?

5 Calculation of eigenfaces with PCA

1. 5.1 Step 1: Prepare the data.
2. 5.2 Step 2: Subtract the mean.
3. 5.3 Step 3: Calculate the covariance matrix.
4. 5.4 Step 4: Calculate the eigenvectors and eigenvalues of the covariance matrix.
5. 5.5 Step 5: Select the principal components.

What is Eigen feature?

Eigen-features from a covariance matrix of a point set with the sample mean are commonly used geometric features that can describe the local geometric characteristics of a point cloud and indicate whether the local geometry is linear, planar, or spherical.

Who were sirovich and Kirby?

But let’s look at one of the first face recognition algorithms developed by mathematicians Larry Sirovich and Michael Kirby at Brown University in the 1980s. They started by computing an average face from a set of pictures.

How does Fisherface algorithm work?

Fisherfaces algorithm extracts principle components that separates one individual from another. So , now an individual’s features can’t dominate another person’s features. LDA is used to find a linear combination of features that separates two or more classes or objects.

Why PCA is used in face recognition?

PCA is a statistical approach used for reducing the number of variables in face recognition. In PCA, every image in the training set is represented as a linear combination of weighted eigenvectors called eigenfaces. A number of experiments were done to evaluate the performance of the face recognition system.

Why is LBP used?

Local Binary Pattern (LBP) is a simple yet very efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. It was first described in 1994 (LBP) and has since been found to be a powerful feature for texture classification.

What is eigenvalue in principal component analysis?

An eigenvalue is a number, telling you how much variance there is in the data in that direction, in the example above the eigenvalue is a number telling us how spread out the data is on the line. In fact the amount of eigenvectors/values that exist equals the number of dimensions the data set has.

What is the meaning of eigenface?

Freebase(0.00 / 0 votes)Rate this definition: Eigenface. Eigenfaces are a set of eigenvectors 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.

When was the eigenface method of face recognition developed?

In 1991 M. Turk and A. Pentland expanded these results and presented the eigenface method of face recognition.

Why are some eigenfaces darker than others?

As one can see, some of these difference faces are darker than others. For whatever reason (perhaps some faces are in slightly more agreeable positions), the lighter faces are more “unique” in this sample of faces. We will see this notion come up again when we compute the actual eigenfaces: some will resemble the more variable faces.

Why are eigenfaces used in the covariance matrix?

The eigenfaces themselves form a basis set of all images used to construct the covariance matrix. This produces dimension reduction by allowing the smaller set of basis images to represent the original training images.