Facial age estimation via extended curvature Gabor filter

Facial age estimation is a process of identifying the age of a single face in an image or a video. Since age information can be used in many environments such as security, surveillance, and entertainment, age estimation has recently received much attention from researchers. In this paper, we propose an automatic age estimation method via extended curvature Gabor (ECG) features and a learning-based technique. Instead of conventional Gabor Filters, we use ECG filters to extract curvature information from a face image, which is useful for estimating age. We use a feature selection method to reduce the computational complexity and prove the effectiveness of ECG features at the same time. We use a regression algorithm to estimate the age of the test face image. As a result, our work achieves a competitive performance compared with other recent works in terms of age estimation.