CLASSIFICATION OF AGE USING THE WRINKLE DENSITIES OF FACE IMAGES WITH GENETIC ALGORITHM AND SUPPORT VECTOR MACHINE - Lee, Dong-Woo; Ohn, Syng-Yup; Na, Jong-Whoa; Heo, Chan (International Journal of Industrial Engineering: Theory, Applications and Practice, 2017)
This paper proposes a new technique to estimate age with a support vector machine (SVM) based on the wrinkle densities of face images. Wrinkle density is defined as the ratio of an area of wrinkles on a section of face skin. In the new method, faces are segmented into eight wrinkle sections based on face geometry. Wrinkle densities are then calculated from each of the wrinkle sections. Next, an age classification model is created using a support vector machine algorithm. The model estimates the age of the face by classifying the face into one of three age classes. The set of features – i.e. the densities of the eight wrinkle sections – and the SVM kernel parameters used in the classification model are optimized with a genetic algorithm (GA) to maximize estimation accuracy. The proposed technique is tested using a face database at Korea Aerospace University (KAU). As a classification model, it shows superior performance over the artificial neural net (ANN) and naïve Bayesian method. Simulation results are also provided, including a comparison between the SVM with GA model and the other models.