Integrating Feature Extractors for the Estimation of Human Facial Age

Facial feature extraction algorithms play an important role in many applications of face biometrics such as face recognition for person identification, classification of emotions by facial expression recognition and age estimation using facial images. In this paper, an integration of different type of feature extraction algorithms is applied on facial images for accurate age estimation. This integration is performed by using two-level fusion of features and scores with the help of feature-level and score-level fusion techniques. In our proposed method, the advantage of using different types of features such as biologically inspired features, texture-based features, and appearance-based features is used. Feature-level fusion of biologically inspired and texture-based methods is integrated into the proposed method and their combination is fused with an appearance-based method using score-level fusion. Experiments on the publicly available MORPH and FG-NET databases prove the effectiveness of the proposed method and the proposed method outperforms many of the state-of-the-art systems.