【摘要】：模糊是一種最常見的圖像退化之一。對于模糊的人臉,通常面臨著兩大任務:模糊人臉識別和模糊人臉恢復。目前的大量工作都只針對其中某一個任務,只有極少量的工作將兩個任務一起考慮。本文分析研究了模糊人臉識別和恢復之間的相輔相成關系。對于模糊人臉恢復,提出了兩個新的基于模糊人臉識別結果的模型,分別解決了當前最好的基于樣例人臉去模糊方法的兩個缺陷。模型1利用訓練類內(nèi)差詞典的線性表示解決了人臉類內(nèi)差距問題,模型2利用梯度的L0.8先驗替代L0先驗解決了清晰的純?nèi)四槄^(qū)域先驗約束問題。兩個模型級聯(lián)成兩步方案解決基于人臉識別的模糊人臉恢復問題。對于模糊人臉識別,綜合對比了模糊和去模糊方法,選擇利用模糊方法和LPQ特征做人臉識別解決基于模糊人臉恢復的模糊人臉識別問題。最后,將恢復與識別構成一個良性循環(huán),提出了一個模糊人臉同步恢復與識別(Simultaneous Blurred Face Restoration and Recognition,SRR)算法,迭代完成模糊人臉恢復與識別。本文提出的SRR算法適用于復雜模糊核情況下的人臉去模糊問題,還適用于小樣本情況下的模糊人臉識別問題。在FERET數(shù)據(jù)庫上的實驗表明,對于多種不同的模糊,SRR算法不僅大幅提高了模糊人臉識別的準確率,還大大提升了模糊人臉恢復的質(zhì)量。
[Abstract]:Blur is one of the most common image degradation. For fuzzy faces, there are usually two tasks: fuzzy face recognition and fuzzy face restoration. Much of the current work is focused on only one of these tasks, with very little work considering the two tasks together. This paper analyzes and studies the complementary relationship between fuzzy face recognition and restoration. For fuzzy face restoration, two new models based on fuzzy face recognition results are proposed, respectively, to solve the two defects of the best face de-blurring method based on sample examples. Model 1 uses the linear representation of the training intra-class difference dictionary to solve the intra-class gap problem. Model 2 uses gradient L0.8 priori to replace L0 priori to solve a clear priori constraint problem in pure face regions. The two models are cascaded into two steps to solve the fuzzy face restoration problem based on face recognition. For fuzzy face recognition, fuzzy and de-fuzzy methods are compared synthetically, and fuzzy face recognition problem based on fuzzy face restoration is solved by using fuzzy method and LPQ feature as face recognition method. Finally, a fuzzy face synchronous recovery and recognition (Simultaneous Blurred Face Restoration and Recognition,SRR) algorithm is proposed, which is composed of a benign cycle of restoration and recognition, and the fuzzy face recovery and recognition is completed iteratively. The SRR algorithm proposed in this paper is suitable for the human face deblurring problem in the case of complex fuzzy kernel and for the fuzzy face recognition problem in the case of small samples. Experiments on FERET database show that SRR algorithm not only improves the accuracy of fuzzy face recognition, but also improves the quality of fuzzy face restoration.
【學位授予單位】：北京郵電大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.41

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