Age estimation using active appearance model combining with local texture features

Abstract

In this paper, a novel age estimation method by using active appearance model (AAM) combining with local texture feature is presented, which overcomes the drawbacks of the AAM. Use the multi-scale local binary patterns (MLBP) as the local texture descriptors to get the rotation invariant texture features. Build the combined AAM model using MLBP features. In this way, both global face features and local texture features are used. The support vector regression (SVR) is used to estimate the facial age. The face aging data set FG-NET is used. Experimental results demonstrate the AAM combined MLBP method performing a lower mean-absolute error (MAE) and high accuracy of estimation comparing to other method results.