我們提出透過Wasserstein損失函數的生成對抗網路輔助擷取人臉屬性特徵(Disentangle Represenetation of Generative Adversarial Network)進行跨角度人臉辨識(Cross Pose Face Recognition)，DR-WGAN藉由Wasserstein-1距離以及梯度懲罰的訓練機制取代DRGAN中的Jensen-Shannon (JS) divergence對DRGAN的訓練穩定度以及影像生成的品質上進行效能的提升，由於考量到Wasserstein-1距離以及梯度懲罰的訓練，因此整個生成與對抗的網絡需要重新設計與改良，在研究與實驗的流程中，我們觀察不同設置上的改變對於網路效能上的影響，包括1)資料正規化、2)激活函數、3)訓練資料的增量，並於論文中重點說明網路重新設計上需要考量的問題，我們探討兩種不同的網路設計，其一由生成網路(G)與對抗網路(D)所組成而第二項則基於的一項加入一分類網路(C)，實驗中DR-WGAN於MPIE標準資料庫中擊敗DRGAN與其他優秀的演算法，在CFP實驗中證實加入了額外的分類器於DR-WGAN中相較於單純由G與D所組合的網路有更好的效能提升。

We propose the Disentangled Representation Learning on a Wasserstein Generative Adversarial Network with Gradient Penalty, or abbreviated as the DR-WGAN, for handling cross-pose face recognition. The proposed DR-WGAN has improved the state-of-the-art DR-GAN (Disentangled Representation Learning GAN) in the quest for the disentangled facial representation good for cross-pose recognition. The DR-WGAN considers the Wasserstein-1 distance and gradient penalty in the design of the discriminator, instead of the Jensen-Shannon (JS) divergence considered in the DR-GAN, substantially improves the training stability, and in turn the generated image quality. As the Wasserstein-1 distance and gradient penalty considered in the discriminator, the overall generative and adversarial framework needs to be redesigned. We have studied the effects of different approaches for data normalization, activation functions, and data augmentation, and highlight the issues to be considered in the redesigned framework. Two structures of the redesigned frameworks are studied, one with a generator and a discriminator, and the other with an additional classifier. Experiments on the MPIE database show that the DR-WGAN outperforms the DR-GAN and other state-of-the-art approaches. Experiments on the CFP database shows that the framework with an additional classifier outperforms the one without.

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