Laura Costa Pereira Miranda 
, Carlos Eduardo Thomaz , Diego Barreto Haddad , Ana Cláudia Souza Vidal de Negreiros
& Gilson Antonio Giraldi
Abstract: The main objective of this work is to investigate the efficiency of data weighting techniques for calculating weighted principal components in gender and facial expression experiments, considering classification and reconstruction problems. Specifically, the methodology consists of generating spatial weights (statistical maps) to weight the pixels of the input images. Then, the weighted data are used as input for the principal component analysis (P CA) algorithm. We will consider the following techniques for calculating spatial weights: (a) Student’s t-test; (b) Hyperplanes computed using SVM (Support Vector Machine); (c) Shannon entropy calculated pixel-by-pixel; (d) Inverse Shannon entropy calculated pixel-by-pixel; (e) Jensen-Shannon divergence. The application of techniques (a), (c), (d), (e) for calculating spatial weights in face recognition is the main contribution of this work. The evaluation of the efficiency of the different principal components obtained with each technique is done by analyzing the results of image reconstruction and classification over FEI and FERET databases. For classification, the following
classifiers were used: K-Nearest Neighbors and Mahalanobis Distance. A quadtree-based methodology was also used to reduce small local variations in the statistical maps, to test its effect on reconstruction and classification.
Keywords: Face Recognition, PCA, Weighting Methods, Classification. Reconstruction.
DOI code: 10.21528/lnlm-vol23-no2-art3
PDF file: vol23-no2-art3.pdf
BibTex file: vol23-no2-art3.bib