Facial age feature extraction based on deep sparse representation

In recent years, the research on facial age estimation has attracted more and more interests from the scholars and researchers in the field of psychological, aesthetic, forensic science and computer vision. However, facial age estimation is a challenging work: face age does not suffer from only the influence of intrinsic factors (e.g., genes) but also external factors (e.g., living conditions), so that it is difficult to find accurate features which can describe the change of age. Therefore, this paper proposes a robust face feature extraction method based on dynamic deep sparse representation. Combined with the respective characteristics of Active Appearance Model (AAM), Local Binary Patterns (LBP), Gabor and Bio-Inspired Features (BIF), this method will give sufficient consideration to the thinking way of object recognition of the human, similarity of adjacent ages and the classification principle of signal sparse representation. In addition, in order to reduce the interference of the face identity factor, two factors analysis method is proposed to separate the face identity factor. The experimental results show that the feature extraction method proposed in this paper has strong discrimination and robustness, which outperforms the state-of-the-art age estimation approaches.