Cyborg groups enhance face recognition in crowded environments
Cyborg groups enhance face recognition in crowded environments
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DOI：
10.1371/journal.pone.0212935
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发表时间：
2019-03-06
期刊：
影响因子：
3.7
通讯作者：
Poli, Riccardo
中科院分区：
文献类型：
--
作者：
Valeriani, Davide;Poli, Riccardo
在拥挤的环境中识别一个人对人类和机器视觉算法来说都是一项具有挑战性但至关重要的视觉搜索任务。本文探讨了将残差神经网络（ResNet），脑机接口（BCI）和人类参与者相结合以创建改善决策的“机器人”的可能性。人类参与者和ResNet进行了相同的面部识别实验。脑机接口用于从脑电信号中解码人类的决策信心。不同类型的半机械人群体被创建，包括只有人类（有或没有BCI）或人类和ResNet的群体。半机械人群体的决策是通过置信度估计来权衡个体决策的。结果表明，机器人群体的准确率（高达35%）明显高于ResNet，平均参与者和没有技术帮助的同等规模的人类群体。这些结果表明，融合人类，BCI和机器视觉技术可以显着改善现实场景中的决策。
Recognizing a person in a crowded environment is a challenging, yet critical, visual-search task for both humans and machine-vision algorithms. This paper explores the possibility of combining a residual neural network (ResNet), brain-computer interfaces (BCIs) and human participants to create "cyborgs" that improve decision making. Human participants and a ResNet undertook the same face-recognition experiment. BCIs were used to decode the decision confidence of humans from their EEG signals. Different types of cyborg groups were created, including either only humans (with or without the BCI) or groups of humans and the ResNet. Cyborg groups decisions were obtained weighing individual decisions by confidence estimates. Results show that groups of cyborgs are significantly more accurate (up to 35%) than the ResNet, the average participant, and equally-sized groups of humans not assisted by technology. These results suggest that melding humans, BCI, and machine-vision technology could significantly improve decision-making in realistic scenarios.