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A. GENERAL ABSTRACTS 1 2 A 2 a THE INT£RACTIVE GENERATION OF FACIAL IMAGES ON A CRT USING A HEURISTIC STRATEGY by ~ a r K Lee Gillenson 197h Ph.D. Thesis Department of Computer and Information Science The Ohio State University Columbus, OIIio ~Ketching a human face is a task which involves spatial Oecisions and a Knowledge Of the aspects of the face that are important in recognition. These are talents WhiCh non-artists lack. This dissertation describes the design and implementation ol a man-machine system with which a non-artist can create, on a graphic display, any male Caucasian facial image from a photograph in front of him. The computer system contains pro-stored facial features, an average face used as a starting point, and a neuristic strategy which provides the user with the "now-to-do-it" Knowledge necessary. The user makes all of the visual aecisions and makes changes in the displayed image hy adjusting analog input devices. The face is subdivided into 17 features, 7 of which are paired, e.g., the ears. Stored in the system are several variations of each feature. These are desiguea such that given the capabilities of the hardware to scale, translate, and rotate images, any reasonable variation can oe achieved for any of the features. The strategy includes some global heuristics in addition to a predetermined sequence of operations. The user always works in the context of one continually upuated face on the CRT. The sequence o f operations is such that the most important changes in terms o f recognition a r e maue first in an attempt to home-in on the target as quickly as possible. The user communicates wltn the system through the analog devices and through single letter or number responses on the alphanumeric terminal, and so is not requires to Know any programming or %o type long instructions. The system begins oy displaying a mathemat~cally calculated average face on the CR~. First the user is asked to estimate the target's age and an aging process tares place. Then a pair o f large scale stretching routines are used to secure the gross shape of the £ace. Furtner, gross changes are made next on individual features or sets of features througn a nlerarcnlcal manipulation scheme. Following this there begins a series of "retrieval" routines. The user is asked questions about each feature and a new version of the feature is retrieved %rom memory and displayed on the G~. It is set at the same scale and position as the previously displayed version o f that feature and can then be further manipulated by the user, In between any pair o£ retrieval routines the user can interrupt to the hierarchy or to any feature retrieval routine out of its usual order. Finally, curly, wavY, or straight hair are added through the use of special routines and the eyebrows and eyeballs are filled in. Experiments were conducted which showed that for an arbitrary non-artls%, nls results on the system in an attempt to sketch a face ~rom a photograph, were superior to his results using pencil and paper. page i~ April 197~ SIGART ,aWS&~T~R

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Review of "Pattern Recognition, Learning, and Thought by Leonard Uhr", Departments of Psychology and Computer Science, University of Washington, Seattle, in SCIENCE.