人臉反欺騙運用預訓練及多分支 CNN;Face Anti Spoofing Using Autoencoder Pretraining In Multi-Branch CNN

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/81287

Title:	人臉反欺騙運用預訓練及多分支 CNN;Face Anti Spoofing Using Autoencoder Pretraining In Multi-Branch CNN
Authors:	阮功信;Cong, Tin Nguyen
Contributors:	資訊工程學系
Keywords:	深度學習;色彩空間;deep learning;color space
Date:	2019-08-16
Issue Date:	2019-09-03 15:42:43 (UTC+8)
Publisher:	國立中央大學
Abstract:	本文提出了一種基於深度學習算法的人臉分類系統。該系統能夠將真實和假面部與普通相機拍攝的 RGB 圖像區分開. 為此,我們構建了一個由 4 部分組成的系統:RGB 圖像處理,HSV 圖像處理, YCrCb 圖像處理和分類。對於圖像處理的前 3 個部分,模型將具有要考慮的對象的不同視點,使得分類可以使得最準確的結論成為可能。此外,為了實現最佳處理性能,我們還包括編碼器和解碼器結構模型,它們消除了不必要的組件 , 並幫助模型僅關注它所提供的組件。很重要,最重要的是,這種結構有助於降低模型的複雜性。在實驗過程中,我們發現數據處理中出現了一些問題,即研究數據與實際數據不符。為了創建一個在實際研究和運營數據上取得良好結果的模型,我們在進行培訓之前對數據進行了一些特殊的調整。實驗結果表明,我們的系統在公共數據庫上給出了非常高的結果。 ;In this thesis, we propose a face classification system based on deep learning algorithm. This system is capable of distinguishing real and fake faces from RGB images taken by a normal camera. To do that, we have built a system of 4 parts: RGB image processing, HSV image processing, YCrCb image processing, and classification. With the first 3 parts of image processing, the model will have different viewpoints of the object to be considered so that the classification can make the most accurate conclusion possible. In addition, in order to achieve optimal processing performance, we include encoder and decoder structure models, which eliminate unnecessary components and help the model focus only on the components it gives. is important, and most importantly, this structure helps reduce the complexity of the model. In the process of experimentation, we found some problems arising in the processing of data, namely that the research data does not match the actual data. In order to create a model for good results on actual research and operational data, we have applied a number of special tweaks to the data before being put into training. Experimental results indicate that our system gives a very high result on public databases.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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