What is eigenface 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.
Why is PCA used?
PCA helps you interpret your data, but it will not always find the important patterns. Principal component analysis (PCA) simplifies the complexity in high-dimensional data while retaining trends and patterns. It does this by transforming the data into fewer dimensions, which act as summaries of features.
How is linear algebra used in facial recognition?
Face-processing neurons seem to use linear algebra, representing faces as vectors in a 50-dimensional vector space. Researchers can use neural activity to predict the face an animal is looking at.
What is the difference between Haar Cascade and CNN?
A Haar-Feature is just like a kernel in CNN, except that in a CNN, the values of the kernel are determined by training, while a Haar-Feature is manually determined.
What is eigenfaces method for face recognition?
In this paper, Eigenfaces method is used for face recognition. In the recognition process, an eigenface is formed for the given face image, and the Euclidian distances between this eigenface and the previously stored eigenfaces are calculated.
What is the face recognition algorithm?
This algorithm take in consideration the features that differentiate one individual from other. It concentrates on the features that represent all the faces of all the people.
How to generate a set of eigenfaces using PCA?
A set of Eigenfaces can be generated by performing a mathematical process of PCA, where it identifies variations in face images in an entire image space as a single point in n×n-dimensional image space. These vectors are called Eigenvectors.
What are the disadvantages of eigenfaces algortihm?
In eigenfaces illumination is also considered an important feature of the face which actually isn’t and due to this some main features are discarded considering them less important. This is a major disadvantage of the eigenfaces algortihm which was later fixed by fisherfaces and LBPH algorithm.