A New Hybrid Model to Predict Human Age Estimation from Face Images Based on Supervised Machine Learning Algorithms

Abstract Age estimation from face images is one of the significant topics in the field of machine vision, which is of great interest to controlling age access and targeted marketing. In this article, there are two main stages for human age estimation; the first stage consists of extracting features from the face areas by using Pseudo Zernike Moments (PZM), Active Appearance Model (AAM), and Bio-Inspired Features (BIF). In the second step, Support Vector Machine (SVM) and Support Vector Regression (SVR) algorithms are used to predict the age range of face images. The proposed method has been assessed utilizing the renowned databases of IMDB-WIKI and WIT-DB. In general, from all results obtained in the experiments, we have concluded that the proposed method can be chosen as the best method for Age estimation from face images.