Towards automatic face identification robust to ageing variation

A large number of high-performance automatic face recognition systems have been reported in the literature. Many of them are robust to within class appearance variation of subjects such as variation in expression, lighting of subjects such as variation in expression, lighting and pose. However, most of the face identification systems developed are sensitive to changes in the age of individuals. We present experimental results to prove that the performance of automatic face recognition systems depends on the age difference of subjects between the training and test images. We also demonstrate that automatic age simulation techniques can be used for designing face recognition systems, robust to ageing variation. In this context, the perceived age of the subjects in the training and test images is modified before the training and classification procedures, so that ageing variation is eliminated. Experimental results demonstrate that the performance of our face recognition system can be improved significantly, when this approach is adopted.