Select Language

AI社区

公开数据集

Labeled Faces in the Wild

Labeled Faces in the Wild

172.2M
476 浏览
0 喜欢
1 次下载
0 条讨论
Face 2D Box

Welcome to Labeled Faces in the Wild, a database of face photographsdesigned for studying the problem of unconstrained f......

数据结构 ? 172.2M

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    Welcome to Labeled Faces in the Wild, a database of face photographs designed for studying the problem of unconstrained face recognition. The data set contains more than 13,000 images of faces collected from the web. Each face has been labeled with the name of the person pictured. 1680 of the people pictured have two or more distinct photos in the data set. The only constraint on these faces is that they were detected by the Viola-Jones face detector. More details can be found in the technical report below.

    There are now four different sets of LFW images including the original and three different types of "aligned" images. The aligned images include "funneled images" (ICCV 2007), LFW-a, which uses an unpublished method of alignment, and "deep funneled" images (NIPS 2012). Among these, LFW-a and the deep funneled images produce superior results for most face verification  algorithms over the original images and over the funneled images (ICCV 2007).


    Related:
    [new] Collected resources related to LFW - updated 2017/05/09.
    LFW Deep Funneled Images.
    LFW attributes file (see Attribute and Simile Classifiers for Face Verification, Kumar et al.).
    Face Detection Data set and Benchmark (FDDB), our new database for face detection research.
    Faces in Real-Life Images workshop at the European Conference on Computer Vision 2008, run by Erik Learned-Miller, Andras Ferencz, and Frederic Jurie.

    Contact:

    Support:

    • The building of the LFW database was supported by NSF CAREER Award number 0546666.


    ×

    帕依提提提温馨提示

    该数据集正在整理中,为您准备了其他渠道,请您使用

    注:部分数据正在处理中,未能直接提供下载,还请大家理解和支持。
    暂无相关内容。
    暂无相关内容。
    • 分享你的想法
    去分享你的想法~~

    全部内容

      欢迎交流分享
      开始分享您的观点和意见,和大家一起交流分享.
    所需积分:10 去赚积分?
    • 476浏览
    • 1下载
    • 0点赞
    • 收藏
    • 分享