Linear discriminant analysis for face recognition

Face is the most common biometric identifier used by humans. During the past thirty years, a number of face recognition techniques have been proposed, all of these methods focus on image-based face recognition that use a still image as input data. In this paper, Linear Discriminant Analysis (LDA) which is also called fisherface is an appearance-based technique used for the dimensionality reduction and recorded a great performance in face recognition. This method works on the same principle as the eigenface method (PCA).it performs dimensionality reduction while preserving as much of the class discriminatory information as possible. LDA makes use of projections of training images into a subspace defined by the fisher faces known as fiherspace. Recognition is performed by projecting a new face onto the fisher space, The KNN algorithm is then applied for identification.