Automatic age estimation from face images is a topic of growing interest nowadays, because of its great value in various applications. The main challenge in automatic facial age estimation task comes from the large intra-class facial appearance variations due to both gender and race attributes. To this end, in this paper we propose a complete approach for age estimation based on demographic classification. The proposed approach consists of three main parts: (1) Automatic face detection and alignment to extract only the regions of interest. (2) Feature extraction from facial region images using Multi-level face representation. (3) Two-Stages age Estimation (TSE). The main idea of TSE is to classify the input face image into one of demographic classes, then estimate age within the identified demographic class. The experimental results demonstrate that our proposed approach can offer better performance for age estimation when compared to the state-of-the-art methods on MORPH-II, PAL and a subset of LFW databases.