Linear Discriminant Analysis

A statistical method used in pattern recognition and machine learning that finds a linear combination of features to best separate two or more classes of objects. In the context of face recognition, LDA (also known as the Fisherfaces method) projects face images into a lower-dimensional space that maximises the ratio of between-class variance to within-class variance, making it particularly effective at distinguishing between individuals despite variations in lighting, pose, and facial expression. LDA has been widely used in assistive technology applications such as wearable face recognition systems for people who are blind. Not to be confused with Latent Dirichlet Allocation, a different technique used in topic modeling.