English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 71578/104447 (69%)
造訪人次 : 19158638      線上人數 : 382
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    政大機構典藏 > 理學院 > 資訊科學系 > 期刊論文 >  Item 140.119/111003
    請使用永久網址來引用或連結此文件: http://nccur.lib.nccu.edu.tw/handle/140.119/111003


    題名: A novel comparative deep learning framework for facial age estimation
    作者: 余能豪
    Abousaleh, Fatma S.;Lim, Tekoing;Cheng, Wen-Huang;Yu, Neng-Hao;Hossain, M. Anwar;Alhamid, Mohammed F.
    貢獻者: 資科系
    關鍵詞: Deep learning;Facial age estimation;Region convolutional neural network;Comparative framework
    日期: 2016-12
    上傳時間: 2017-07-12 14:14:02 (UTC+8)
    摘要: Developing automatic facial age estimation algorithms that are comparable or even superior to the human ability in age estimation becomes an attractive yet challenging topic emerging in recent years. The conventional methods estimate one person’s age directly from the given facial image. In contrast, motivated by human cognitive processes, we proposed a comparative deep learning framework, called Comparative Region Convolutional Neural Network (CRCNN), by first comparing the input face with reference faces of known age to generate a set of hints (comparative relations, i.e., the input face is younger or older than each reference). Then, an estimation stage aggregates all the hints to estimate the person’s age. Our approach has several advantages: first, the age estimation task is split into several comparative stages, which is simpler than directly computing the person’s age; secondly, in addition to the input face itself, side information (comparative relations) can be explicitly involved to benefit the estimation task; finally, few incorrect comparisons will not influence much the accuracy of the result, making this approach more robust than the conventional approach. To the best of our knowledge, the proposed approach is the first comparative deep learning framework for facial age estimation. Furthermore, we proposed to incorporate the Method of Auxiliary Coordinates (MAC) for training, which reduces the ill-conditioning problem of the deep network and affords an efficient and distributed optimization. In comparison to the best results from the state-of-the-art methods, the CRCNN showed a significant outperformance on all the benchmarks, with a relative improvement of 13.24% (on FG-NET), 23.20% (on MORPH), and 4.74% (IoG).
    關聯: EURASIP Journal on Image and Video Processing, 2016(1), 47
    資料類型: article
    DOI: http://dx.doi.org/10.1186/s13640-016-0151-4
    顯示於類別:[資訊科學系] 期刊論文

    文件中的檔案:

    檔案 大小格式瀏覽次數
    47.pdf1971KbAdobe PDF21檢視/開啟


    在政大典藏中所有的資料項目都受到原著作權保護.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回饋