徐繼聖 / Gee-Sern Hsu (Jison Hsu)

隨著深度學習方法的進步，面部年齡轉換算法的發展已成為計算機視覺領域的一個有吸引力的研究主題。給定一張面部圖像作為輸入，面部年齡轉換指的是生成與輸入面部相同身份但年齡較大或較小的面部圖像，同時保持輸入面部的身份特徵。這是一個具有挑戰性的任務，由於面部外觀變化是由於生理老化過程而引起的固有複雜性，與個體的身體狀況、性別、種族和其他因素有關。近年來，基於生成對抗網絡（GAN）的方法、大型面部年齡數據集的可用性以及商業潛力，已經引起越來越多的關注。

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Due to the recent progress made by state-of-the-art deep learning approaches, the development of facial age transformation algorithms has become an attractive research topic in the fields of computer vision. Given a face as input, the facial age transformation refers to the generation of facial images for the input face but with older or younger ages in the sense that the identity of the input face can be well preserved. This is a challenging task due to the intrinsic complexity of the facial appearance variation caused by the physical aging process, which can be related to individual physical condition, gender, race and other factors. It has received increasing attention in recent years because of the effectiveness of the Generative Adversarial Network (GAN) based approaches, the availability of large facial age datasets and commercial potentials.