國立虎尾科技大學 |

Wu tou jing jia mi guang xiao jie he shen jing wang lu zhi ren lian bian shi = = Facial Recognition System with Lensfree Encrypted Optics and Neural Network.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Wu tou jing jia mi guang xiao jie he shen jing wang lu zhi ren lian bian shi =/
Reminder of title:	Facial Recognition System with Lensfree Encrypted Optics and Neural Network.
remainder title:	Facial Recognition System with Lensfree Encrypted Optics and Neural Network.
Author:	Fang, Yi-Chun.
Description:	1 online resource (56 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-02.
Contained By:	Masters Abstracts International84-02.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798841513766

Wu tou jing jia mi guang xiao jie he shen jing wang lu zhi ren lian bian shi = = Facial Recognition System with Lensfree Encrypted Optics and Neural Network.
Fang, Yi-Chun.

Wu tou jing jia mi guang xiao jie he shen jing wang lu zhi ren lian bian shi =Facial Recognition System with Lensfree Encrypted Optics and Neural Network.Facial Recognition System with Lensfree Encrypted Optics and Neural Network. - 1 online resource (56 pages)

Source: Masters Abstracts International, Volume: 84-02.

Thesis (M.S.)--National Yang Ming Chiao Tung University, 2021.

Includes bibliographical references

We proposed a face recognition system based on optical encryption, which can prevent privacy concern where the client's face was disclosed directly in the process. We applied a coded mask as for the optical encryption. A sensor in the lensless imaging system captured blurry but still informative intermediate images. The decryption system combined with deep learning-based classifier to learn the face features from the blurry encrypted images successfully. The whole process does not contain any original face images, so it can reduce the risk of personal data leakage. We designed two experiments using neural networks as classifier models. One was a simulation experiment using a monitor to display a face dataset and another utilized actual face for experiment. In the simulation experiment, two different coded masks were tested for performance. Both trained classifiers for respective encrypted images achieve recognition accuracy of 97.5% for testing set, which verified that the deep learning model can successfully learn effective face features from encrypted images. Then we discuss the limitation of working distance in the system and its solutions. Finally, the recognition accuracy reached 94.4% in the actual face experiment with ideal environment. The experiments reveal the potential of our proposed encrypted face recognition system for practical use case. We believe that the system can be continuously improved and will have more widely applications in the future.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798841513766Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Lensless imaging systemIndex Terms--Genre/Form:

554714
Electronic books.

Wu tou jing jia mi guang xiao jie he shen jing wang lu zhi ren lian bian shi = = Facial Recognition System with Lensfree Encrypted Optics and Neural Network.
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500 $a Source: Masters Abstracts International, Volume: 84-02.
500 $a Advisor: Tai, Ya-Hsiang ; Tien, Chung-Hao.
502 $a Thesis (M.S.)--National Yang Ming Chiao Tung University, 2021.
504 $a Includes bibliographical references
520 $a We proposed a face recognition system based on optical encryption, which can prevent privacy concern where the client's face was disclosed directly in the process. We applied a coded mask as for the optical encryption. A sensor in the lensless imaging system captured blurry but still informative intermediate images. The decryption system combined with deep learning-based classifier to learn the face features from the blurry encrypted images successfully. The whole process does not contain any original face images, so it can reduce the risk of personal data leakage. We designed two experiments using neural networks as classifier models. One was a simulation experiment using a monitor to display a face dataset and another utilized actual face for experiment. In the simulation experiment, two different coded masks were tested for performance. Both trained classifiers for respective encrypted images achieve recognition accuracy of 97.5% for testing set, which verified that the deep learning model can successfully learn effective face features from encrypted images. Then we discuss the limitation of working distance in the system and its solutions. Finally, the recognition accuracy reached 94.4% in the actual face experiment with ideal environment. The experiments reveal the potential of our proposed encrypted face recognition system for practical use case. We believe that the system can be continuously improved and will have more widely applications in the future.
520 $a 本篇論文提出一種採用光學加密的人臉辨識系統，在人臉辨識的過程中，可以有效避免直接擷取清晰人臉所造成的相關隱私問題。我們利用編碼光罩作為光學加密的光學元件，透過此無透鏡成像系統中的感測器所擷取模糊但仍保有資訊的中繼影像，並結合深度學習分類器對中繼影像進行學習，最後得以成功辨識資料庫中的人臉影像。整個取樣過程不包含任何清晰的人臉影像，因此可以降低流出個資的風險。我們設計了兩種使用基於神經網路作為分類器模型的實驗，分別為使用顯示器播放人臉資料庫的仿真實驗與人臉實拍實驗。仿真實驗中，測試兩種不同編碼光罩加密的模糊影像資料庫，兩個加密影像的分類器對於其測試集都有97.5%的正確率，驗證了深度學習模型可以成功對編碼影像成功學習被加密的人臉特徵。接著探討了系統的工作距離限制與解決方案。最後，人臉實拍實驗中，在較理想的環境下可以達到94.4%的正確率。在實驗中驗證了我們的加密人臉辨識系統具有實用的潛力，相信在未來我們所提出的系統可以持續改進並擁有更廣泛的應用。
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650 4 $a Optics. $3 595336
653 $a Lensless imaging system
653 $a Coded mask
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653 $a 人臉辨識
653 $a 編碼光罩
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880 1 0 $6 245-02/$1 $a 無透鏡加密光學結合神經網路之人臉辨識 = $b Facial Recognition System with Lensfree Encrypted Optics and Neural Network.