How do face recognition algorithms work?

How do face recognition algorithms work?

Some face recognition algorithms identify facial features by extracting landmarks, or features, from an image of the subject’s face. For example, an algorithm may analyze the relative position, size, and/or shape of the eyes, nose, cheekbones, and jaw. A probe image is then compared with the face data.

What is Fisherface algorithm?

Fisherface is one of the popular algorithms used in face recognition, and is widely believed to be superior to other techniques, such as eigenface because of the effort to maximize the separation between classes in the training process.

What is Eigenfaces algorithm?

An eigenface (/ˈaɪɡənˌfeɪs/) is the name given to a set of eigenvectors when used in the computer vision problem of human face recognition. The eigenvectors are derived from the covariance matrix of the probability distribution over the high-dimensional vector space of face images.

Why is PCA used?

Principal Component Analysis (PCA) is used to explain the variance-covariance structure of a set of variables through linear combinations. It is often used as a dimensionality-reduction technique.