Synthetic faces, face cubes, and the geometry of face space

Abstract

To simplify the study of visual face processing, we introduce a novel class of synthetic face stimuli based upon 37 measurements (head shape, feature locations, etc.) extracted from individual face photographs in both frontal and 20 degrees side views. Synthetic faces are bandpass filtered optimally for face perception and include both line and edge information. Pilot experiments establish that subjects are extremely accurate in matching a synthetic face with the original grayscale photograph, even across views. To determine the perceptual metric of face space, we introduce face cubes in which the geometric differences between any faces in a four-dimensional face subspace can be precisely determined. Experiments on face discrimination using face cubes establish the metric of synthetic face space as locally Euclidean, with discrimination thresholds representing 4-6% total geometric variation (as a percent of mean head radius) between faces. Discrimination thresholds are lowest for face cubes constructed around the average face, thus indicating that the mean face for each gender represents a natural origin for face space. Finally, synthetic faces exhibit a pronounced inversion effect for 20 degrees side views and a characteristic "Thatcher effect" for inverted front views. Synthetic faces and face cubes thus provide a useful new quantitative approach to the study of face perception and face space.

Recognition memory for realistic synthetic faces

“…That same approach would likely fail with face stimuli because the representational space is clearly anisotropic. For example, the difference threshold for discriminating between faces varies substantially from one region of face space to another, with smallest difference thresholds in the neighborhood of the average face (Wilson et al, 2002). To permit full expression of potential anisotropies, we used nonmetric MDS to characterize the perceptual space within which our face stimuli were located and to quantify the distances between faces in that space.…”

“…We chose to work with realistic, synthetic human faces generated using methods introduced by Wilson, Loffler, and Wilkinson (2002). As test stimuli, Wilson faces mitigate problems arising from the availability of nonfacial information or from the availability of distinctive featural differences among faces (e.g., Duchaine & Weidenfeld, 2003;Sadr, Jarudi, & Sinha, 2003).…”

“…To reinforce reliance on visual information per se, we limited rehearsal by permitting subjects only a brief glimpse of each face and then allowing only a short interval between successive faces. Wilson's synthesized faces are geometrically simple, as compared with actual, unprocessed grayscale photographs of faces, but these synthesized faces manage to convey sufficient information to permit identification of individual faces (Wilson et al, 2002). This individuality, which is important in episodic memory, is absent from some commonly used face stimuli, such as Brunswik faces (Brunswik & Reiter, 1937).…”

Preservation of Episodic Visual Recognition Memory in Aging

“…Human faces are one obvious example of such stimuli, but if this proposed study were carried out, it would be crucial to use faces whose metric properties could be controlled in a metric fashion, as we did with compound gratings. Wilson, Loffler, and Wilkinson (2002) recently introduced novel computer-synthesized human faces that seem quite suitable for this purpose.…”

A new synthetic face generation method for gender discrimination

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A new parametric method for generating synthetic faces is proposed in this study which could be used for psychophysics studies on face perception [1]. Two separate programs, one in Delphi 2005 programming environment and another in MATLAB ® is developed to sample real faces and generating synthetic faces respectively.

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