Human Age Estimation via Geometric and Textural Features

Aging progress of a person is influenced by many factors such as genetics, health, lifestyle, and even weather conditions. Therefore human age estimation from a face image is a challenging problem. Aging causes significant variations in facial shape and texture across years. In order to construct a general age classifier, shape and texture information of human face should be used together. In this paper, we propose a new age estimation system that uses a number of overlapping age groups and a classifier that combine geometric and textural facial features. The classifier scoring results are interpolated to produce the estimated age. We tested many geometric and textural facial features with age group classifiers. Comparative experiments show that the best performance is obtained using the fusion of Local Gabor Binary Patterns and Geometric features.