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.
