Objectives  Face age estimation is still challenging because facial age image are not only affected intrinsic factors (e.g. genes), but also external factors (e.g. living conditions). Therefore, it is difficult to find features which can describe the variation of age accurately.

  Methods  We propose a robust face age analysis method based on multi-layer sparse representation, which combines the respective characteristics of active appearance model (AAM), local binary patterns (LBP) and bio-inspired features (BIF). This method will fully consider the way of object recognition of the human, the similarity of adjacent ages and the classification principle of signal sparse representation. In addition, in order to reduce the interference from the face identity factor, two-factors-analysis method is proposed to separate the face identity factor.

  Results  A large number of experiments are conducted on the public FG-NET, IFDB and MORPH2 datasets, and the mean absolute errors are within 4.65 years and 3.64 years for FG-NET and MORPH2 database, respectively.

  Conclusions  The experimental results show that the multi-layer sparse representation model has strong discrimination and robustness, which overcomes the shortcomings of traditional methods.