以範例引導式之網格變形為基礎之高解析度人臉模型年輕化

Abstract

臉部特徵是我們認識其他人的第一印象，包含許多個人訊息如年齡及吸引程度，雖然目前科學研究仍無法明確定義出絕對的臉部美學，但已有研究指出一個人臉對其他人的吸引程度並不受限於文化或是社會審美觀(a cross-culture criteria)，而越年輕的人臉也隱含對其他人越具吸引力。臉部老化有四個重要特徵:眼皮下垂、雙頰凹陷、臉部肌肉鬆垮以及皺紋，除了膚色外臉部輪廓也會隨著年紀增長而變化，在高解析度的立體模型(high-resolution 3D model)進一步分析年紀對人臉輪廓的影響，應該能得到更好的年輕化效果。本篇論文提出一個以引導式範例為基礎的網格變形方法以增加高解析度人臉模型的吸引力(enhance facial attractiveness)，在較年長的模型上取出特徵點間的長度，如上述與年齡相關，再利用學習得到的數學老化模型以求取較年輕的特徵向量並盡量使之與原始資料相近，該組特徵長度即表示一較年輕模型該有的臉部比例，以此為根據將較年長人臉模型變形至較年輕人臉，最後的結果，我們也提出各種不同的分析圖表。

Human face conveys significant characteristics of a person such as age traits and facial attractiveness. The absolute aesthetic value for human faces is still unclear; but meanwhile, facial attractiveness has been considered as cross-culture criteria. De-aging procedure on 3D model geometry can be used to enhance facial attractiveness due to improved shape analysis. We conclude our observation on aging progress into four rules: 1. drooping of eyelids, 2. lack of elasticity causing hollow cheeks, 3. slack on the middle face and 4. wrinkles. In this paper we present a framework to beautify (enhance facial attractiveness) realistic facial model by a mesh deformation method based on data-driven approach. For an elder input face, we extract the edges connected among feature points on the face mesh. The edge lengths are used as a feature vector, and an age score is associated with it. We search for a local minimum to obtain a related young vector and consequently maintain the similarity between the elder and young face. Once the young length vector has been determined, we can embed it to our new feature locations and then deform the model to a younger shape. The effectiveness of our work is to rejuvenate an elder face by altering the outline of a facial model.