Face recognition is widely used in many computer vision applications such as surveillance, traffic monitoring, robot vision, access control and so on. However, some problems in face recognition still exist which the expression changes, head movements, accessory occlusion, light changes and aging effect are the main issues. For the aging effect, the shape and texture changes degrade the performance of face recognition. To solve the issue in across age face recognition, we propose face discriminative methods across age progression using local K-means ensemble. First, we find that the gradient angle provides an effective representation. This representation can be improved using hierarchical structure, the K-means pyramid (KMP). KMP demonstrates excellent performance when combined with supervised learning. Experimental results show that the proposed across age methods outperform the existing techniques.