In this paper, a feature extraction model for face recognition is proposed. This model is constructed by implementing three biologically inspired strategies, namely a hierarchical network, a learning mechanism of the V1 simple cells, and a data-driven attention mechanism. The hierarchical network emulates the functions of the V1 cortex to progressively extract facial features invariant to illumination, expression, slight pose change, and variations caused by local transformation of facial parts. In the network, filters that account for the local structures of the face are derived through the learning mechanism and used for the invariant feature extraction. The attention mechanism computes a saliency map for the face, and enhances the salient regions of the invariant features to further improve the performance. Experiments on the FERET and AR face databases show that the proposed model boosts the recognition accuracy effectively.