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High-performing face recognizers use eye-eye distance and eye-nose distance more than low-performing face recognizers

机译:高效能的面部识别器比低效的面部识别器使用更多的眼-眼距离和鼻-鼻距离

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It is widely understood that faces are recognized either holistically or configurally, in terms information that is spatially distributed across much of the face. Individuals with developmental prosopagnosia demonstrate profound difficulty when recognizing faces whereas "super recognizers" are individuals with exceptional ability to remember faces with very little effort (Russell & Nakayama, 2006). The current study explores face recognition in super recognizers in more detail, focusing independently on face discrimination ability and face recognition decision strategy using a two-part experiment. Face recognition ability was assessed using the Cambridge Face Memory Test (Duchaine & Nakayama, 2006) and participants who performed in the top 10% and bottom 10% were operationally defined as high- and low-performing face recognizers, respectively. In Part 1 of the experiment, we measured face discrimination thresholds for seven internal face dimensions (i.e. eye shape, nose-mouth distance, etc) using a 2-interval same-different task. Face stimuli were displayed in grayscale, and an adaptive thresholding procedure (QUEST) was used to measure the 79%-correct threshold for face discrimination. In Part 2 of the experiment, we measured face recognition strategy using a 3-alternative forced error (3-AFE) design, a modified 3-AFC, delayed match-to-sample task in which there is no correct answer (Schwartz & Chang, 2008). Distractors were calibrated using thresholds measured in Part 1 and each distractor varied in only one dimension from the target so participant response indicated the dimension with the least utility. Results show that high-performing face recognizers rely more on eye-eye distance and eye-nose distance compared to low-performing face recognizers. High performers also deemphasize eye shape and nose-mouth distance compared to low performers.
机译:广泛理解的是,就空间上分布在面部的大部分上的信息而言,面部被整体地或配置地识别。具有发育前期障碍的个体在识别脸部时表现出极大的困难,而“超级识别者”是具有极强的能力来记住脸部的非凡能力的人(Russell&Nakayama,2006)。当前的研究更详细地探索了超级识别器中的人脸识别,并通过两部分实验独立地关注人脸识别能力和人脸识别决策策略。使用剑桥人脸记忆测试(Duchaine和Nakayama,2006年)对人脸识别能力进行了评估,在操作上排名前10%和后10%的参与者分别被定义为性能高和低的人脸识别器。在实验的第1部分中,我们使用两个间隔相同的任务测量了七个内部面部尺寸(即眼睛形状,鼻口距离等)的面部识别阈值。面部刺激以灰度显示,并使用自适应阈值处理程序(QUEST)来测量79%正确阈值的面部识别能力。在实验的第2部分中,我们使用3-替代强制错误(3-AFE)设计,改进的3-AFC,延迟匹配采样任务(没有正确答案)来测量人脸识别策略(Schwartz和Chang ,2008)。使用第1部分中测量的阈值对干扰物进行校准,并且每个干扰物与目标之间仅在一个维度上发生变化,因此参与者的响应表明该维度的效用最小。结果表明,与低性能的面部识别器相比,高性能的面部识别器更多地依赖于眼眼距离和眼鼻距离。与低绩效者相比,高绩效者也不再强调眼睛的形状和嘴巴距离。

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