Comparison between illumination normalization methods used to improve the rate of facial recognition / Comparação entre métodos de normalização de iluminação utilizados para melhorar a taxa do reconhecimento facial

AUTOR(ES)
DATA DE PUBLICAÇÃO

2008

RESUMO

Distinct lighting conditions in an image can produce unequal representations of the same object, compromising segmentation and pattern recognition processes, including facial recognition. Hence, the lighting distribution on an image is considered of great importance, and normalization algorithms using new techniques have still been researched. This research aims to evaluate the following illumination normalization algorithms found in literature: LogAbout, variation of homomorphic filter and wavelet based method. The main interest was to find out the illumination normalization method which improves the facial recognition rate. The algorithms used for face recognition were: eigenfaces, PCA (Principal Component Analysis) with LVQ neural network and wavelets with MLP (Multilayer Perceptron) neural network. Images from Yale Face Database B, divided into three subsets have been used. The results show that the wavelet and LogAbout technique provided the best facial recognition rate. Experiments showed that the illumination normalization methods, in general, improve the facial recognition rate, except for the variation of homomorphic filter technique with the algorithms: eigenfaces and PCA with LVQ.

ASSUNTO(S)

auto-faces illumination normalization methods artificial neural networks reconhecimento facial face recognition wavelets métodos de normalização da iluminação pca redes neurais artificiais wavelets eigenfaces pca

ACESSO AO ARTIGO

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