本篇論文提出混合不同人臉區塊的年齡估測架構並結合移動分類視窗和軟邊界回歸進行人臉年齡估測。我們比較了多個人臉辨識網路在年齡估測的效能，並以混合架構結合不同人臉區塊的深度學習特徵，提出移動式分類視窗以決定邊界年齡，再以軟邊界回歸以提升年齡估測的準確度。我們將提出的方法測試在評估實際年齡的資料庫Morph，和評估外表年齡的LAP及Adience資料庫上，均達到優於大多近期方法的結果。

We propose the Region-based Hybrid Framework (RHF) with moving segmentation and soft-boundary regression for age estimation. The RHF is an ensemble of VGG networks, and each VGG net considers a specific facial region as input. The VGG is selected from a comparison of pretrained facial models originally designed for face recognition, but trained again for age estimation by transfer learning. To improve the accuracy of RHF, we implement two schemes, the moving segmentation and soft boundary regression. The moving segmentation better determines the boundary ages good to segment the age. The soft boundary regression can rectify the age estimate that is falsely classified by the moving segmentation. The proposed approach is validated by experiments on MORPH, LAP and Adience, and compared to the state-of-the-art methods to demonstrate its efficacy.

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