Face Recognition Methods Based on Feedforward Neural Networks, Principal Component Analysis and Self-Organizing Map
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In this contribution, human face as biometric is considered. Original method of feature extraction from image data is introduced using MLP (multilayer perceptron) and PCA (principal component analysis). This method is used in human face recognition system and results are compared to face recognition system using PCA directly, to a system with direct classification of input images by MLP and RBF (radial basis function) networks, and to a system using MLP as a feature extractor and MLP and RBF networks in the role of classifier. Also a two-stage method for face recognition is presented, in which Kohonen self-organizing map is used as a feature extractor. MLP and RBF network are used as classifiers. In order to obtain deeper insight into presented methods, also visualizations of internal representation of input data obtained by neural networks are presented.
KeywordsBiometrics, face recognition, neural networks, PCA, multilayer perceptron, radial-basis function network, self-organizing map, visualization, LDA, kernels
Document typePeer reviewed
Document versionFinal PDF
SourceRadioengineering. 2007, vol. 16, č. 1, s. 51-57. ISSN 1210-2512
- 2007/1