As people age, the physical morphology of the face does change. Hence, face ageing is complex and therefore raises significant challenges for computer-based models to create accurate and realistic-looking aged or de-aged faces. Our experimental results do suggest that the proposed approach achieves accuracy, efficiency and possess flexibility when it comes to facial age progression or regression.Īs far as the ageing of the face is concerned, lifestyle- and health-related factors are known to affect the process of physical ageing. ![]() We have utilised two datasets, namely the FEI and the Morph II, to test, verify and validate our approach. To do this, we have utilised a pre-trained convolutional neural network based on the VGG-face model for feature extraction, and we then use well-known classifiers to compare the features. To validate our approach, we compute the similarity between aged images and the corresponding ground truth via face recognition. The resulting image is controlled by two parameters corresponding to the texture and the shape of the face. Thus, given a face image, the target aged image for that face is generated by applying it to the relevant template face image. We use template faces based on the formulation of an average face of a given ethnicity and for a given age. In this paper, we propose a novel approach to try and address this problem. ![]() Over the past decade or so, researchers have been working on developing face processing mechanisms to tackle the challenge of generating realistic aged faces for applications related to smart systems. As such, automatic aged or de-aged face generation has become an important subject of study in recent times. Techniques for facial age progression and regression have many applications and a myriad of challenges.
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