A novel robust feature extraction with GSO-optimized extreme learning for age-invariant face recognition

This paper presents a novel age function modelling technique on the basis of the fusion of local features obtained by local texture descriptors. Initially, image normalization is performed and a feature extraction process is carried out. The age estimation performances of new texture descriptors Local Phase Quantization, Weber Local Descriptor and the familiar texture descriptor Local Binary Patterns, which are not examined thoroughly for age estimation modelling, are analysed in this paper. Then the feature fusion process is taken place for investigating the age estimation precisions of various concatenation of the local texture descriptors. By using PCA, dimensionality reduction is implemented for reducing the dimensions of the images. Extreme Learning Machine (ELM) classifier is applied to evaluate the output images for the corresponding input images. Because of the mild optimization restrictions, ELM can be simple for execution and normally attains the finer generalization performance. The outcomes display that, when compared with the earlier techniques, the age function modelling accuracy of the developed system is better.