本論文從全臉與臉部區塊提取仿生特徵來進行年齡估測的比較。本研究發現
全臉特徵在年齡估測中是占有最多影響力的部分，但如果一些局部區塊與全臉的
特徵結合可以有效提高整體的效能。將本方法測試在三個不同種族的年齡資料庫
後發現，那些有影響力的臉部區塊會依據種族的不同而產生差異。本論文也包含
了對分層式年齡分類(Hierarchical Age Estimation)的研究，此分類方法主要分為兩
層，在第一層是分類年齡群組是屬於年輕族群或成人族群，並在第二層使用偏最
小二乘回歸（PLSR）來進行細部的年齡估測。研究的重點式探討要如何對第一
層兩個年齡群組的分割邊界做定義才可以提高年齡估測的精度。將本方法在公開
年齡資料庫，如FG-NET 與MORPH 上作測試，以證實其有效性。

The Bio-Inspired Features (BIFs), extracted from holistic and facial components, are compared for the performance of age estimation. Although it is discovered from this study that the holistic accounts for the most influential part, a few components are found to be able to improve the overall performance when combining with the holistic features. It is also revealed that the influential facial components vary with ethnicity, based on the experiments on three databases with different ethnic backgrounds. The approach nourished from this study consists of a hierarchical structure with two processing layers. The first layer segments a face into young and adult groups, and the second layer estimate the age using Partial Least Square Regression (PLSR). How to determine the segmentation boundary in the first layer is also studied for the improvement of the estimation accuracy. The performance of this approach is validated on publicly available databases to reveal its effectiveness.

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