ABSTRACT 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.

中文翻译：

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一种新的鲁棒特征提取，具有 GSO 优化的极限学习，用于年龄不变的人脸识别

摘要 本文提出了一种新的年龄函数建模技术，它基于局部纹理描述符获得的局部特征的融合。首先，执行图像归一化并执行特征提取过程。本文分析了新纹理描述符局部相位量化、韦伯局部描述符和熟悉的纹理描述符局部二进制模式的年龄估计性能，这些都没有对年龄估计建模进行彻底检查。然后进行特征融合过程以研究局部纹理描述符的各种串联的年龄估计精度。通过使用PCA，实现了降维以降低图像的维度。极限学习机 (ELM) 分类器用于评估相应输入图像的输出图像。由于温和的优化限制，ELM 可以很容易执行，并且通常可以获得更好的泛化性能。结果表明，与早期技术相比，所开发系统的年龄函数建模精度更好。