MATLAB Simulation of Face Age Estimation Using Support Vector Machine

In this document, I will demonstrate how to perform MATLAB simulation for face age estimation using Support Vector Machine (SVM). Facial age estimation represents a fascinating and practical research domain that enables us to extract age-related information from facial images. Employing SVM as the core algorithm proves highly effective for this task due to its robust classification capabilities in handling high-dimensional feature spaces. Throughout the simulation process, we will utilize MATLAB programming language, a powerful and widely-adopted computational tool particularly suited for image processing and machine learning implementations. The implementation will involve key steps including facial feature extraction using techniques like Gabor filters or Local Binary Patterns, data preprocessing for age group labeling, SVM model training with parameter optimization using functions like fitcsvm, and age classification validation. This simulation experiment will provide comprehensive insights into SVM algorithm principles while offering hands-on experience in practical application development. Key MATLAB functions to be explored include image preprocessing routines, feature extraction methods, and SVM classification with cross-validation techniques. Let's begin the implementation!