|Christopher J. Morris|
An Experimental Analysis of the Pre-Attentiveness of Features in Chernoff FacesChristopher J. Morris, David S. Ebert, and Penny Rheingans
SPIE Proceedings of Applied Imagery Pattern Recognition `99: 3D Visualization for Data Exploration and Decision Making.
Chernoff faces have been proposed as a tool for scientifc and information visualization. However, the effectiveness of this form of visualization is still open to speculation. Chernoff faces, it is suggested, make use humans' apparently inherent ability to recognize faces and small changes in facial characteristics. Limited research has been conducted 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 certain features are pre-attentive. Furthermore, what effect a certain number of distracters (i.e. more faces) have of 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 conducted a user study, which test the effectiveness and pre-attentiveness of several features of Chernoff faces. Our user study indicated that for longer viewing times (two seconds), eye size and eyebrow slant were the most accurate features. These initial results indicate that Chernoff faces may have a significant advantage over other iconic visualization techniques for multidimensional information visualization.
DownloadSPIE Proceedings - Copyright © 1999 Society of Photo-Optical Instrumentation Engineers. This paper was (will be) published in [add relevant proceedings information] and is made available as an electronic reprint [preprint] with permission of SPIE. Single print or electronic copies for personal use only are allowed. Systematic or multiple reproduction, distribution to multiple locations through an electronic listserver or other electronic means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are all prohibited. By choosing to view or print this document, you agree to all the provisions of the copyright law protecting it.
|About IBM | Privacy | Legal | Contact|