Biologically-Inspired Aging Face Recognition Using C1 and Shape Features

To deal with the variations caused by age, an aging face recognition method Based on HMAX model, which motivated by a quantitative model of visual cortex, was proposed to achieve temporal invariance. First, each face image was normalized to a standard size. Second, the C1-S features, which preserve facial texture and shape information, were defined by facial key points and HMAX model to represent the face image with the high dimensional features. Then C1-S features are projected to a low dimensional subspace by PCA. Finally, the nearest neighbor rule with Mahalanobis distance was used to aging face recognition from rank 1 to rank 6. Experiments on the FG-NET database show that our proposed C1-S features are good at tolerating local position, scale and aging variations and improve the accuracy of aging face recognition.