Data dimensionality reduction for face recognition
Georgy Kukharev, Paweł Forczmański · Machine Graphics and Vision · 2004
In the process of image recognition in most of the applications there is a problem with gathering, processing and storing large amounts of data. A possible solution for reducing this amounts and speeding-up computations is to use some sort of data reduction. Efficient reduction of the stored data without losing any important part of it requires an adaptive method, which works without any supervision. In this article we discuss a few variants of a two-step approach, which involves Karhunen-Loeve Transform (KLT) and Linear Discriminant Analysis (LDA). The KLT gives a good approximation of the input data, however it requires a large number of eigenvalues. The second step reduces data dimensionality further using LDA. The efficiency of KLT depends on the quality and quantity of the input data. In the case when only one image in a class is given as input, its features are not stable in comparison with other images in other classes. In the article we present a few methods for solving this problem, which improve on the ideas presented in [6,9].