Howtosignalprocessing
- How does PCA algorithm work in face recognition?
- What is eigenfaces in face recognition?
- How does the eigenface algorithm work?
- What are the limitations of doing face recognition with eigenfaces?
How does PCA algorithm work 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. These eigenvectors are obtained from covariance matrix of a training image set.
What is eigenfaces in face recognition?
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.
How does the eigenface algorithm work?
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.
What are the limitations of doing face recognition with eigenfaces?
Additionally, the eigenface recognition method bears some common disadvantages due to its ``appearance-based'' nature. First, learning is very time-consuming, which makes it difficult to update the face database.
