What do Eigenfaces represent?

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.

What is Eigenfaces PCA?

Eigenfaces is a method that is useful for face recognition and detection by determining the variance of faces in a collection of face images and use those variances to encode and decode a face in a machine learning way without the full information reducing computation and space complexity.

Is PCA based on Eigenfaces?

A set of eigenfaces can be generated by performing a mathematical process called principal component analysis (PCA) on a large set of images depicting different human faces. Informally, eigenfaces can be considered a set of “standardized face ingredients”, derived from statistical analysis of many pictures of faces.

What is the role of eigen vectors in PCA?

The eigenvectors and eigenvalues of a covariance (or correlation) matrix represent the “core” of a PCA: The eigenvectors (principal components) determine the directions of the new feature space, and the eigenvalues determine their magnitude.

How the Eigenfaces are used in human face detection?

The strategy of the Eigenfaces method consists of extracting the characteristic features on the face and representing the face in question as a linear combination of the so called ‘eigenfaces’ obtained from the feature extraction process. The principal components of the faces in the training set are calculated.

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.

Which technique is based on Eigenfaces?

The algorithm has to work successfully even with the above challenges. In Table 1, a comparison of some of the methods used for face recognition based on the number of images in the training set and the resulting success rate is provided. The basis of the eigenfaces method is the Principal Component Analysis (PCA).

How is PCA 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. The face images must be centered and of the same size.

What is Fisherface algorithm for face recognition?

Image recognition using fisherface method is based on the reduction of face space dimension using Principal Component Analysis (PCA) method, then apply Fisher’s Linear Discriminant (FDL) method or also known as Linear Discriminant Analysis (LDA) method to obtain feature of image characteristic.