5 May 2000 Experimental analysis of the effectiveness of features in Chernoff faces
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Proceedings Volume 3905, 28th AIPR Workshop: 3D Visualization for Data Exploration and Decision Making; (2000) https://doi.org/10.1117/12.384865
Event: 28th AIPR Workshop: 3D Visualization for Data Exploration and Decision Making, 1999, Washington, DC, United States
Abstract
Chernoff faces have been proposed as a tool for scientific and information visualization. However, the effectiveness of this form of visualization is still open to speculation. Chernoff faces, it is suggested, make use of humans' apparently inherent ability to recognize faces and small changes in facial characteristics. Limited research has been conduced to assess how well Chernoff faces make use of this ability. So far, it is still unclear how humans recognize faces and whether or not a specific set of rules governs the process. A particular area of interest is whether or not certain features are pre-attentive. Furthermore, what effect a certain number of distractors have on the attentiveness of various features is also of concern. This information could be used to maximize the effectiveness of Chernoff faces by providing an indication of which applications would be best served by the use of Chernoff faces. In order to address this issue, we have conduced a user study, which tested the effectiveness and pre-attentiveness of several features of Chernoff faces. Our user study indicated that the perception of eye size, a specific face, eyebrow slant, and the combination eyebrow slant with eye size is a serial process. Our study also indicated that for longer viewing times, eye size and eyebrow slant were the most accurate features. These initial results indicate that Chernoff faces may not have a significant advantage over other iconic visualization techniques for multidimensional information visualization.
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Christopher J. Morris, Christopher J. Morris, David S. Ebert, David S. Ebert, Penny L. Rheingans, Penny L. Rheingans, } "Experimental analysis of the effectiveness of features in Chernoff faces", Proc. SPIE 3905, 28th AIPR Workshop: 3D Visualization for Data Exploration and Decision Making, (5 May 2000); doi: 10.1117/12.384865; https://doi.org/10.1117/12.384865
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