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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/84091

    Title: 人臉圖片生成與增益之可用性與效率探討分析
    Authors: 江翰霖;Chiang, Han-Lin
    Contributors: 資訊管理學系在職專班
    Keywords: 人臉辨識;資料生成;資料增益;Face detection;Data generation;Data augmentation
    Date: 2020-07-28
    Issue Date: 2020-09-02 18:03:27 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 人臉辨識一直是相當熱門的話題,而在近年機器學習技術的興起,更將人臉辨識推往更上一層樓。經由大量的數據做訓練,能夠有效的提升辨識準確度,因此如何更大量的收集人臉辨識訓練圖片,便是一門重要的課題。



    ;Face detection has always been a very popular topic, and in recent years, the rise of machine learning technology has pushed face detection to a higher level.
    Training through a large amount of data can effectively improve the detection accuracy, so how to collect a larger number of face detection training pictures is an important topic.
    In response to the research and development plan, the case of A Company in this study adopted a supervised learning method in the face detection project to train the face pictures to generate face detection modules, so there is often a demand for a large number of face pictures. Under long-term demand, the use of data generation and data augmentation is a very suitable way.
    This study focuses on face data generation and face data augmentation, and explores which of the two methods is more efficient in producing face images, including the time it takes to generate the images and the availability of the generated images.
    Comparing the two, we can find that the two are similar in terms of usability, but in terms of time efficiency, the way of using face data augmentation is much faster than the way of using face generation.
    After confirming the efficiency and availability, it will be of great help to the future development of A company in this research case.
    Appears in Collections:[資訊管理學系碩士在職專班 ] 博碩士論文

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