Facial age estimation using hybrid Haar wavelet and color features with Support Vector Regression

Face appearance is one of the most important visual features of human which varies significantly over the aging. Therefore, automatic age estimation is a demanding research topic in the field of facial feature analysis. In the task of age estimation, feature extraction is the first influential step which highly effects on a learning method and its obtained results. The second important step of an age estimation system is training of pattern recognition method based on the extracted feature vector. Considering the importance of the feature extraction and training steps, this paper utilizes the combination of Haar wavelet transform and color moment approaches to extract full-informative and influencing feature elements of face image. To improve the training step, the paper trains a Support Vector Regression (SVR) model, based on the extracted feature vector for age estimation. Experimental results of the proposed method are performed on FG-NET and MORPH datasets and prove the superiority of the method compared with the state-of-the-art methods.