分層式年齡估測常都結合分類與回歸，先以假設的分界線分類數個不同的年齡群組，再在每一個群組內以回歸估測年齡。目前的研究較少深入探討年齡分界線，而且不同的研究對假設的年齡分界線大多不同。另外年齡回歸運算大多也局限在兩個年齢分界線內的部分，也就是單一年齡群組中的年齡不會出現在其他年齡族群，我們稱這樣的年齡邊界為硬邊界，硬年齡邊界忽略了一個非常重要的事實，即相近年齡的外觀應十分接近，年齢分界線的兩側均有外觀類似的人臉。本論文提出以移動式分類和軟邊界回歸的方式，有效的定義年齡族群分界線與更精確的估測年齡，並結合全人臉與局部特徵進一步提高分類率。所提出的方法經標準年齡資料庫之測試，含FG-NET與MORPH，確認其有效性。

Many hierarchical approaches for age estimation combine classification and regression. In the classification phase, a few age groups with age boundaries are postulated without much interpretation. The age boundaries are different one another across different works, and it is hard to determine which are better. In the regression phase, the age boundaries are handled as hard boundaries, i.e., the regression in each age group only covers the ages within that age group, and does not go beyond the age boundaries. Hard boundaries ignore an important fact that faces of close ages look similar and such a similarity exists for all ages, including boundary ages. A moving segmentation approach and soft boundary regression are proposed to better define age groups and estimate ages. Combining the holistic and component features and comparing different ways to increase the classification rate, the proposed method is experimentally proven able to outperform state-of-the-art approaches on benchmark databases.

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