Is PCA good for face recognition?

This approach is preferred due to its simplicity, speed and learning capability [2]. One of the simplest and most effective PCA approaches used in face recognition systems is the so-called eigenface approach. The problem is limited to files that can be used to recognize the face.

Why dimensionality reduction?

It reduces the time and storage space required. It helps Remove multi-collinearity which improves the interpretation of the parameters of the machine learning model. It becomes easier to visualize the data when reduced to very low dimensions such as 2D or 3D. It avoids the curse of dimensionality.

Why is LDA dimensionality reduction?

LDA reduces dimensionality from original number of feature to C — 1 features, where C is the number of classes. LDA basically projects the data in a new linear feature space, obviously the classifier will reach high accuracy if the data are linear separable.

How is PCA used for dimensionality reduction?

Principal Component Analysis(PCA) is one of the most popular linear dimension reduction algorithms. “PCA works on a condition that while the data in a higher-dimensional space is mapped to data in a lower dimension space, the variance or spread of the data in the lower dimensional space should be maximum.”

What is the use of PCA in machine learning?

PCA calculates the Eigen vectors of the covariance matrix, and projects the original data onto a lower dimensional feature space, which is defined by Eigen vectors with large Eigen values. PCA has been used in face representation and recognition where the Eigen vectors calculated are referred to as Eigen faces.

What is PCA used for in computer vision?

PCA has been used in face representation and recognition where the Eigen vectors calculated are referred to as Eigen faces. In gel images, even more than in human faces, the dimensionality of the original data is vast compared to the size of the dataset, suggesting PCA as a useful first step in analysis.

What is the difference between PCA and LDA?

Difference between PCA and LDA: The prime difference between LDA and PCA is that LDA deals directly with discrimination between classes, whereas the PCA deals with the data in its entirety for the principal components analysis without paying any particular attention to the underlying class structure [27].In PCA,…

What is the difference between linear discriminant analysis and PCA?

The goal of the Linear Discriminant Analysis (LDA) is to find an efficient way to represent the face vector space. PCA constructs the face space using the whole face training data as a whole, and not using the face class information. On the other hand, LDA uses class specific information which best discriminates among classes.