Unconstrained face recognition: Establishing baseline human performance via crowdsourcing
Unconstrained face recognition: Establishing baseline human performance via crowdsourcing
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无约束的人脸识别：通过众包建立人类表现基线
DOI：
10.1109/btas.2014.6996296
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Anil K. Jain
中科院分区：
文献类型：
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作者：
L. Best;Shiwani Bisht;Joshua C. Klontz;Anil K. Jain
人脸识别的研究重点已经转移到识别的人脸“在野外”的静止图像和视频中捕获的不受约束的成像环境，没有用户的合作。由于姿势、照明和表情的混杂因素以及遮挡和低分辨率，部署在法医和安全应用中的当前面部识别系统以半自动方式操作;操作员通常查看来自面部识别系统的最佳结果以手动确定最终匹配。因此，分析匹配算法（机器）和人类在无约束人脸识别任务中所达到的准确度是很重要的。在本文中，我们报告人类的准确性不受约束的面孔在静止图像和视频通过众包亚马逊机械土耳其特别是，我们报告的第一个人类的表现在YouTube的面孔数据库，并表明，人类是上级机器，特别是当视频包含上下文线索，除了面部图像。我们调查了来自两个不同国家（美国和印度）的人的准确性，发现来自美国的人更准确，可能是因为他们熟悉YouTube Faces数据库中公众人物的面孔。由人类和商业现成的面部匹配器进行的识别的融合提高了单独人类的性能。
Research focus in face recognition has shifted towards recognition of faces “in the wild” for both still images and videos which are captured in unconstrained imaging environments and without user cooperation. Due to confounding factors of pose, illumination, and expression, as well as occlusion and low resolution, current face recognition systems deployed in forensic and security applications operate in a semi-automatic manner; an operator typically reviews the top results from the face recognition system to manually determine the final match. For this reason, it is important to analyze the accuracies achieved by both the matching algorithms (machines) and humans on unconstrained face recognition tasks. In this paper, we report human accuracy on unconstrained faces in still images and videos via crowd-sourcing on Amazon Mechanical Turk. In particular, we report the first human performance on the YouTube Faces database and show that humans are superior to machines, especially when videos contain contextual cues in addition to the face image. We investigate the accuracy of humans from two different countries (United States and India) and find that humans from the United States are more accurate, possibly due to their familiarity with the faces of the public figures in the YouTube Faces database. A fusion of recognitions made by humans and a commercial-off-the-shelf face matcher improves performance over humans alone.