In this article, we explore the coherence of face perception between human and machine in the scenario of interactive face retrieval. In the part of human perception, we collect user feedback to the stimuli of a target face and groups of displayed candidate face images in a face database with a large number of subjects. In the part of machine vision, we compare the benchmark features and general metrics to measure face similarity. We propose a series of coherence measurements to evaluate the statistic characteristic of human and machine face perception. We discover that despite the unfamiliarity of users to most faces in the database, the coherence between human and machine remains in a stable level across multiple variations in metrics, features, size of databases, and demographics. The simulation experiments with the coherence distributions demonstrate that the embedded information is valuable to speed up interactive retrieval. The comparisons over multiple parameter settings provide feasible instructions in designing the interactive face retrieval system with more consideration of human factors.

中文翻译：

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交互式人脸检索中用户反馈的感知分析

在本文中，我们探讨了交互式人脸检索场景下人与机器之间人脸感知的连贯性。在人类感知部分，我们收集用户对目标人脸刺激的反馈以及在包含大量主题的人脸数据库中显示的候选人脸图像组。在机器视觉部分，我们比较基准特征和通用指标来衡量人脸相似度。我们提出了一系列相干性测量来评估人和机器面部感知的统计特征。我们发现，尽管用户对数据库中的大多数面孔不熟悉，但人与机器之间的一致性在指标、特征、数据库大小和人口统计的多种变化中保持在稳定的水平。相干分布的模拟实验表明嵌入信息对于加速交互式检索是有价值的。多个参数设置的比较为设计交互式人脸检索系统提供了可行的指导，更多地考虑了人为因素。