Face age estimation approach based on deep learning and principle component analysis

Abstract

This paper presents an approach for age estimation based on faces through classifying facial images into predefined age-groups. However, a task such as the one at hand faces several difficulties because of the different aspects of every single person. Factors like exposure, weather, gender and lifestyle all come into play. While some trends are similar for faces from a similar age group, it is problematic to distinguish the aging aspects for every age group. This paper's concentration is in four chosen age groups where the estimation takes place. We employed a fast and effective machine learning method: deep learning so that it could solve the age categorization issue. Principal component analysis (PCA) was used for extracting features and reducing face image. Age estimation was applied to three different aging datasets from Morph and experimental results are reported to validate its efficiency and robustness. Eventually, it is evident from the results that the current approach has achieved high classification results compared with support vector machine (SVM) and k-nearest neighbors (K-NN).