Age Classification using Facial Feature Extraction

This thesis presents age classification on facial images using Local Binary Patterns (LBP) and modular Principal Component Analysis (mPCA) as subpattern-based approaches and holistic Principal Component Analysis (PCA) and holistic subspace Linear Discriminant Analysis (ssLDA) methods. Classification of age intervals are conducted separately on female and male facial images since the aging process for female and male is different for human beings in real life. The age classification performance of the holistic approaches is compared with the performance of subpattern-based LBP and mPCA approaches in order to demonstrate the performance differences between these two types of approaches. Our work has been tested on two aging databases namely FGNET and MORPH. The experiments are performed on these aging databases to demonstrate the age classification performance on female and male facial images of human beings using subpatternbased LBP method with several parameter settings. The results are then compared with the results of age classification using mPCA method, holistic PCA and subspace LDA methods.