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Automatic Age Estimation (AAE) has attracted attention due to the wide variety of possible applications. However, it is a challenging task because of the large variation of facial appearance and several other extrinsic and intrinsic factors. Most of the proposed approaches in the literature use hand-crafted features to encode ageing patterns. Deeply learned features extracted by Convolutional Neural Networks (CNNs) algorithms usually perform better than hand-crafted features. The main contribution of this paper is an extensive comparative analysis of several frameworks for real AAE based on deep learning architectures. Different well-known CNN architectures are considered and their performances are compared. MORPH, FG-NET, FACES, PubFig and CASIA-web Face datasets are used in our experiments. The robustness of the best deep estimator is evaluated under noise, expression changes, "crossing" ethnicity and "crossing" gender. The experimental results demonstrate the high performances of the popular CNNs frameworks against the state-of-art methods of automatic age estimation. A Layer-wise transfer learning evaluation is done to study the optimal number of layers to fine-tune on AAE task. An evaluation framework of Knowledge transfer from face recognition task across AAE is performed. We have made our best-performing CNNs models publicly available that would allow one to duplicate the results and for further research on the use of CNNs for AAE from face images. • An up-to-date literature overview of Age Estimation (AE) methods • A comparative analysis of several frameworks for AE based on deep learning • Different well-known CNNs are considered and their performances are compared • The robustness of the best deep AE is evaluated under intrinsic and extrinsic factors • An evaluation framework of Knowledge transfer for AAE is performed, [ABSTRACT FROM AUTHOR]