臉部屬性輔助之卷積類神經網路於人臉超解析分辨

Attribute Augmented Convolutional Neural Network for Face Hallucination

李承翰(Cheng-Han Lee)

指導教授 ： 徐宏民

國立臺灣大學/電機資訊學院/資訊工程學研究所/碩士(2018年)

https://doi.org/10.6342/NTU201802791

摘要

雖然現有的人臉超解析分辨作法，在整體區域效能評估已經取得優良的效果，但大部份的方法在局部臉部屬性的復原上仍不夠準確，特別在復原極低解析度的人臉相片(14 x 12 像素)。在這篇論文中，我們提出了全新的臉部屬性輔助之卷積類神經網路在人臉超解析分辨的問題上，我們使用了臉部屬性來加強人臉超解析分辨，特別在局部區域復原之強化，使復原之結果更加有描述性。具體來說，我們提出的方法融合了影像領域與臉部屬性的領域，成功地輔助臉部屬性之復原。比起過去的方法，更多的實驗顯示出我們提出的方法不論在整體區域還是局部區域之視覺效果還有量化數據都能達到更好的表現，除此之外，我們提出的AACNN 即使在不知道完整的臉部屬性情況下，也能有很好的復原效果。

關鍵字

超解析分辨 ； 臉部屬性 ； 生成對抗網路

並列摘要

Though existing face hallucination methods achieve great performance on the global region evaluation, most of them cannot recover local attributes accurately, especially when super-resolving a very low-resolution face image from 14 x 12 pixels to its 8 x larger one. In this paper, we propose a brand new Attribute Augmented Convolutional Neural Network (AACNN) to assist face hallucination by exploiting facial attributes. The goal is to augment face hallucination, particularly the local regions, with informative attribute description. More specifically, our method fuses the advantages of both image domain and attribute domain, which significantly assists facial attributes recovery. Extensive experiments demonstrate that our proposed method achieves superior visual quality of hallucination on both local region and global region against the state-of-the-art methods. In addition, our AACNN still improves the performance of hallucination adaptively with partial attribute input.

並列關鍵字

Super Resolution ； Facial Attribute ； Generative Adversarial Network

參考文獻

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