MATLAB Code Implementation for Face Recognition with Gender and Age Classification

Implementing face recognition with gender and age feature extraction in MATLAB is a comprehensive task involving image processing and machine learning. The process begins with preprocessing input images using computer vision techniques, including face detection and alignment. MATLAB's Computer Vision Toolbox provides built-in face detectors (such as vision.CascadeObjectDetector) that can quickly locate facial regions in images through functions like detect() which returns bounding box coordinates. Gender and age feature extraction typically relies on facial feature analysis. For gender recognition, distinct features like facial contours, eyebrow shape, and jawline characteristics can be extracted. Age estimation focuses more on skin texture, wrinkle distribution, and facial muscle relaxation. These features can be extracted using methods like Local Binary Patterns (LBP) implemented via extractLBPFeatures(), Histogram of Oriented Gradients (HOG) using extractHOGFeatures(), or deep learning approaches with pre-trained networks like AlexNet or VGG-16. Support Vector Machine (SVM) is a machine learning method suitable for small-sample classification that performs well in gender and age classification tasks. Before using SVM, extracted feature vectors need normalization (using functions like normalize or zscore) and appropriate kernel selection (linear or Gaussian kernel via fitcsvm with 'KernelFunction' parameter). The trained model can then predict gender and age for new facial images using predict() function. The key to the entire workflow lies in feature extraction accuracy and SVM parameter optimization (using techniques like cross-validation with crossval), which directly impacts final recognition performance. Through proper feature design combined with SVM's strong classification capabilities, an efficient face attribute recognition system can be implemented in MATLAB with a complete pipeline from image input to attribute prediction.