Techniques for facial age progression and regression have many applications and a myriad of challenges. As such, automatic aged or de-aged face generation has become an important subject of study in recent times. 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. In this paper, we propose a novel approach to try and address this problem. We use template faces based on the formulation of an average face of a given ethnicity and for a given age. Thus, given a face image, the target aged image for that face is generated by applying it to the relevant template face image. The resulting image is controlled by two parameters corresponding to the texture and the shape of the face. To validate our approach, we compute the similarity between aged images and the corresponding ground truth via face recognition. 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. We have utilised two datasets, namely the FEI and the Morph II, to test, verify and validate our approach. Our experimental results do suggest that the proposed approach achieves accuracy, efficiency and possess flexibility when it comes to facial age progression or regression.

中文翻译：

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使用示例面部模板的面部年龄进展和回归框架

面部年龄进展和回归技术有许多应用和无数挑战。因此，自动老化或去老化人脸生成已成为近来的一个重要研究课题。在过去十年左右的时间里，研究人员一直致力于开发人脸处理机制，以应对为与智能系统相关的应用生成逼真的老化人脸的挑战。在本文中，我们提出了一种尝试解决这个问题的新方法。我们使用基于给定种族和给定年龄的平均人脸公式的模板人脸。因此，给定一张人脸图像，通过将其应用于相关模板人脸图像来生成该人脸的目标老化图像。生成的图像由对应于面部纹理和形状的两个参数控制。为了验证我们的方法，我们通过人脸识别来计算老化图像和相应地面实况之间的相似性。为此，我们利用基于 VGG-face 模型的预训练卷积神经网络进行特征提取，然后使用众所周知的分类器来比较特征。我们使用了两个数据集，即 FEI 和 Morph II，来测试、验证和验证我们的方法。我们的实验结果确实表明，所提出的方法在面部年龄进展或回归方面实现了准确性、效率和灵活性。我们使用了两个数据集，即 FEI 和 Morph II，来测试、验证和验证我们的方法。我们的实验结果确实表明，所提出的方法在面部年龄进展或回归方面实现了准确性、效率和灵活性。我们使用了两个数据集，即 FEI 和 Morph II，来测试、验证和验证我们的方法。我们的实验结果确实表明，所提出的方法在面部年龄进展或回归方面实现了准确性、效率和灵活性。