DR-SRWGAN: 具備自我主動訓練且適用於虹膜定位與虹膜遮罩生成之超解析度解糾纏華生斯坦生成對抗深度學習網路;Dr-Srwgan: a Self-Trained Disentangle Representation and Super-Resolution Wasserstein Generative Adversarial Networks for Iris Segmentation and Occlusion Estimation

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/84688

Title:	DR-SRWGAN: 具備自我主動訓練且適用於虹膜定位與虹膜遮罩生成之超解析度解糾纏華生斯坦生成對抗深度學習網路;Dr-Srwgan: a Self-Trained Disentangle Representation and Super-Resolution Wasserstein Generative Adversarial Networks for Iris Segmentation and Occlusion Estimation
Authors:	栗永徽
Contributors:	資訊工程學系
Keywords:	深度學習;生物辨識;深度虹膜辨識;虹膜影像切割;虹膜遮罩估測;超解析度;生成對抗式網路;解糾纏網路;自我訓練模型;Deep Learning;Biometric Recognition;Deep Iris Recognition;Iris Segmentation;Iris Occlusion Estimation;Super-Resolution;GAN;Disentangle Representation Network;Self-trained Model
Date:	2020-12-08
Issue Date:	2020-12-09 10:43:59 (UTC+8)
Publisher:	科技部
Abstract:	在以CNN為主軸的影像辨識工作上，如何蒐集大量的影像以供網路的訓練及測試使用，是實務上常遇到的困難。在深度虹膜辨識的模型訓練上也是如此。如何能夠蒐集到足夠多的特例影像來重新訓練神經網路，是一個很重要的議題。虹膜影像本身不易收集，需要特殊的光學設備，而特殊情況的虹膜影像，則更難以收集。在實務上，特殊的虹膜影像常常使得虹膜辨識系統的某個環節失效(例如虹膜定位)。因此收集大量特殊的虹膜影像，對於訓練新的深度學習虹膜辨識演算法，會有極大的幫助。為了產生足夠真實且可以針對特定目的的影像，我們提出了一種具備自我學習功能的生成對抗式網路，稱為 DR-SRWGAN，在此新的GAN架構裡面，我們綜合運用好幾個新近的GAN概念，包括：Pix2Pix, WGAN-GP、Super-Resolution GAN以及DFCN。運用此研究方法，根據使用者自行設定的參數條件，DR-SRWGAN可以根據實驗者的需求，隨機的產生各種各樣特殊類型虹膜影像以及其精確的Groundtruth label, 包括：虹膜內外邊界、虹膜遮罩、虹膜視角偏斜度、是否戴眼鏡等等資訊，藉此解決在進行深度學習實驗時訓練資料影像不足的問題。此研究成果可以應用於：虹膜辨識、其它領域的物體定位或者語意切割、高精確度之眼球追蹤系統、眼科相關疾病之影像分析、虹膜學等等。 ;For research topic like image recognition using CNN, how to collect a large number of images for network training and testing is a common difficulty in practice. The same is true for model training for deep iris recognition. How to collect enough special case images to retrain the neural network is an important issue. The iris image itself is not easy to collect and requires special optical equipment, and the iris image under special conditions is more difficult to collect. In practice, special iris images often fail the iris recognition system (because of the failure in iris segmentation stage). Therefore, collecting a large number of special iris images will be of great help in training new deep iris recognition algorithms.In order to produce images that are sufficiently realistic and can be targeted for specific purposes, we propose a generative adversarial network with self-learning capabilities, called DR-SRWGAN. In this new GAN architecture, we comprehensively apply several recent GAN techniques, including: Pix2Pix, WGAN-GP, Super-Resolution GAN, and DFCN. After properly trained, DR-SRWGAN can randomly generate various special types of iris images and their precise Groundtruth labels according to the needs of the experimenter, including the inner and outer boundaries of the iris, and the iris mask, off-axis gazing angle, and a binary indicator for eye-glasses. In such way, it solves the problem of scarce data problem for deep iris recognition. The research results can also be applied to other areas including: iris recognition, object localization or semantic segmentation, high-precision eye tracking system, image analysis of ophthalmology-related diseases, iridology … etc.
Relation:	財團法人國家實驗研究院科技政策研究與資訊中心
Appears in Collections:	[Department of Computer Science and information Engineering] Research Project

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