人臉驗證技術之所以被廣泛研究，來自於這項技術應用在監視與取證上，它是非常重要的技術之一。本篇論文中，我們的目的是驗證兩張不同情境下拍攝的人臉影像是否為同一個人(如遮蔽的人臉與非遮蔽人臉)。本篇提出的架構中，我們介紹一種基於人臉重建為主的人臉驗證方法；在進行驗證方法前，先將遮蔽的人臉影像進行重建，再將結果進行驗證，重建與驗證的模組均是以樣本學習為主的方法。在重建模組中，我們利用樣本學習機制建立多樣的轉換式，這些轉換式提供了先驗知識能重建任一一張未見過的人臉遮蔽影像。為了確保重建結果能夠保留個人的臉部特徵，我們整合驗證模組於重建器中，對重建的結果進行驗證以這提供更可靠人臉外貌。實驗證明，我們提出的學習架構在遮蔽情境下的人臉驗證有不錯的效果。 In this study, our goal is to verify identity of two facial images that captured under different scenarios (e.g., occluded facial image vs. non-occluded one). We introduce an example-based approach for face verification, i.e. the occluded facial image is recovered first, and then used for face verification process. In the synthesis module, training examples are used to provide a prior knowledge for the unseen occluded image reconstruction. In order to make sure the quality of recovered results, a verification step is embedded. Such discriminative-driven synthesis framework provides more reliable facial appearance for both face reconstruction and verification. According to the experimental results, the proposed system makes the synthesis results preserve the personal characteristics of system input.
