在本篇論文中，我們利用年齡合成(Age synthesis )重新呈現了人類臉部圖像的自然老化現象。我們提出了兩階段方法模擬一個面部衰老的過程：首先，建立一個延伸了顱面生長模型( Craniofacial growth model) 的形狀轉換模型，並且基於心理物理學和人體測量學的研究，觀察並統計面部的五官輪廓隨著年齡的增長的細微變形，該模型主要描述了人類隨時間成長而影響到面部所產生的形狀變化。
接著運用以圖像梯度為原理的演算法，分區擷取特徵並依此建立老化的紋理模型，在這裡使用Poisson image editing進行細部的紋理的模擬轉換，描述了不同年齡的面部的皺紋和其他皮膚紋理，為了得到老齡化合成的結果，我們透過定義特徵點，並用以三角形測量為基礎的分段仿射的變形( Triangulating and piecewise affine warping)來達成，使模擬結果更加的自然且趨近真實性。
我們從FG_NET和MORPH 兩大年齡數據庫中，對人臉圖像做統計分析其特徵點參數來建立模型，並且尋找相似度高的圖片來進行我們的面部老化程序，包含形狀及紋理的模擬合成。在最後，用此篇論文所呈現的模擬結果，與真實的老化面部圖像進行比較與觀察。

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