Facial Age Classification Using Geometric Ratios and Wrinkle Analysis

Age group classification is the process of automatically determining an individual’s age range based on features extracted from facial image. It plays an important role in many real-life applications such as age specific human computer interaction, forensic art, access control and surveillance monitoring, person identification, data mining and organization, and cosmetology. In this thesis, we propose facial age classification approaches based on local and global descriptors extracted through feature selection methods. This thesis proposes two different methods on facial age classification. The first proposed method is a novel and efficient age group classification approach that combines holistic and local features extracted from facial images. These combined features are used to classify subjects into several age groups in two key stages. First, geometric features of each face are extracted to construct a global facial feature. Support Vector Classifier (SVC) is used to classify the facial images into several age groups using computed facial feature ratios. Then, local facial features are extracted utilizing subpattern-based Local Binary Patterns (LBP) to classify adults. These combined features are used to classify subjects into six major age groups. The superiority of subpattern-based LBP over Principal Component Analysis (PCA) and Subspace Linear Discriminant Analysis (subspace LDA)techniques is presented. The second proposed method presents geometric feature-based model for age group classification of facial images. The feature extraction is performed considering significance of the effects that age has on facial anthropometry. In this context, Particle Swarm Optimization (PSO) technique is used to find optimized subset of geometric features. Age Classification on these features is evaluated using SVC. Wrinkle feature analysis is also applied to classify adult images. The facial images are categorized into seven major age groups. The effectiveness and accuracy of the proposed age classification are demonstrated with the experiments that are conducted on two publicly available databases namely Face and Gesture Recognition Research Network (FGNET) and Iranian Face Database (IFDB). The experimental results show significant improvement of the proposed methods compared to the state-of-the-art models. Keywords: Age group classification, feature extraction, Local Binary Patterns, Particle Swarm Optimization.