David W. Hunter

Looking Like a Leader–Facial Shape Predicts Perceived Height and Leadership Ability

Judgments of leadership ability from face images predict the outcomes of actual political elections and are correlated with leadership success in the corporate world. The specific facial cues that people use to judge leadership remain unclear, however. Physical height is also associated with political and organizational success, raising the possibility that facial cues of height contribute to leadership perceptions. Consequently, we assessed whether cues to height exist in the face and, if so, whether they are associated with perception of leadership ability. We found that facial cues to perceived height had a strong relationship with perceived leadership ability. Furthermore, when allowed to manually manipulate faces, participants increased facial cues associated with perceived height in order to maximize leadership perception. A morphometric analysis of face shape revealed that structural facial masculinity was not responsible for the relationship between perceived height and perceived leadership ability. Given the prominence of facial appearance in making social judgments, facial cues to perceived height may have a significant influence on leadership selection.

Distribution of independent components of binocular natural images

An influential theory of the function of early processing in the visual cortex is that it forms an efficient coding of ecologically valid stimuli. In particular, correlations and differences between visual signals from the two eyes are believed to be of great importance in solving both depth from disparity and binocular fusion. Techniques such as independent-component analysis have been developed to learn efficient codings from natural images; these codings have been found to resemble receptive fields of simple cells in V1. However, the extent to which this approach provides an explanation of the functionality of the visual cortex is still an open question. We compared binocular independent components with physiological measurements and found a broad range of similarities along with a number of key differences. In common with physiological measurements, we found components with a broad range of both phase- and position-disparity tuning. However, we also found a larger population of binocularly anticorrelated components than have been found physiologically. We found components focused narrowly on detecting disparities proportional to half-integer multiples of wavelength rather than the range of disparities found physiologically. We present the results as a detailed analysis of phase and position disparities in Gabor-like components generated by independent-component analysis trained on binocular natural images and compare these results to physiology. We find strong similarities between components learned from natural images, indicating that ecologically valid stimuli are important in understanding cortical function, but with significant differences that suggest that our current models are incomplete.

Men's facial masculinity : when (body) size matters

Recent studies suggest that judgments of facial masculinity reflect more than sexually dimorphic shape. Here, we investigated whether the perception of masculinity is influenced by facial cues to body height and weight. We used the average differences in three-dimensional face shape of forty men and forty women to compute a morphological masculinity score, and derived analogous measures for facial correlates of height and weight based on the average face shape of short and tall, and light and heavy men. We found that facial cues to body height and weight had substantial and independent effects on the perception of masculinity. Our findings suggest that men are perceived as more masculine if they appear taller and heavier, independent of how much their face shape differs from womens. We describe a simple method to quantify how body traits are reflected in the face and to define the physical basis of psychological attributions.

Encoding and estimation of first- and second-order binocular disparity in natural images

Highlights • First- and second-order responses to natural binocular images are correlated.• Second-order mechanisms can improve the accuracy of disparity estimation.• Second-order mechanisms can extend the depth range of binocular stereopsis.

Ideal Binocular Disparity Detectors Learned Using Independent Subspace Analysis on Binocular Natural Image Pairs

An influential theory of mammalian vision, known as the efficient coding hypothesis, holds that early stages in the visual cortex attempts to form an efficient coding of ecologically valid stimuli. Although numerous authors have successfully modelled some aspects of early vision mathematically, closer inspection has found substantial discrepancies between the predictions of some of these models and observations of neurons in the visual cortex. In particular analysis of linear-non-linear models of simple-cells using Independent Component Analysis has found a strong bias towards features on the horoptor. In order to investigate the link between the information content of binocular images, mathematical models of complex cells and physiological recordings, we applied Independent Subspace Analysis to binocular image patches in order to learn a set of complex-cell-like models. We found that these complex-cell-like models exhibited a wide range of binocular disparity-discriminability, although only a minority exhibited high binocular discrimination scores. However, in common with the linear-non-linear model case we found that feature detection was limited to the horoptor suggesting that current mathematical models are limited in their ability to explain the functionality of the visual cortex.

Fibre Centred Tensor Faces

In this paper we present a reformulation of the tensorface analysis method and produce a model that is simpler (i.e. has fewer parameters), is more compact (i.e. has tighter distributions) and is less ambiguous (i.e. no 2 sets of parameters synthesise the same data vector). This is achieved by simply subtracting the fibre (row, column, etc) mean from each fibre of the training data before performing PCA analysis. Centring of tensor data via subtraction of the whole set mean is commonly used as a preprocessing step, but the fibre-centring algorithm presented here has not been suggested previously for tensorface analysis. We show how the new formulation allows an approximate linear analysis with a considerable speed improvement over previous methods. In addition, the centring allows simpler truncation of parameter vectors leading to an even more compact model. The new method is tested on image synthesis and analysis and in a simple face recognition task, in which it out performs non-centred multilinear analysis.

The effect of image position on the Independent Components of natural binocular images

Human visual performance degrades substantially as the angular distance from the fovea increases. This decrease in performance is found for both binocular and monocular vision. Although analysis of the statistics of natural images has provided significant insights into human visual processing, little research has focused on the statistical content of binocular images at eccentric angles. We applied Independent Component Analysis to rectangular image patches cut from locations within binocular images corresponding to different degrees of eccentricity. The distribution of components learned from the varying locations was examined to determine how these distributions varied across eccentricity. We found a general trend towards a broader spread of horizontal and vertical position disparity tunings in eccentric regions compared to the fovea, with the horizontal spread more pronounced than the vertical spread. Eccentric locations above the centroid show a strong bias towards far-tuned components, eccentric locations below the centroid show a strong bias towards near-tuned components. These distributions exhibit substantial similarities with physiological measurements in V1, however in common with previous research we also observe important differences, in particular distributions of binocular phase disparity which do not match physiology.

The threshold of binocularity : Natural image statistics explain the reduction of visual acuity in peripheral vision

Visual acuity is greatest in the centre of the visual field, peaking in the fovea and degrading significantly towards the periphery. The rate of decay of visual performance with eccentricity depends strongly on the stimuli and task used in measurement. While detailed measures of this decay have been made across a broad range of tasks, a comprehensive theoretical account of this phenomenon is lacking. We demonstrate that the decay in visual performance can be attributed to the efficient encoding of binocular information in natural scenes. The efficient coding hypothesis holds that the early stages of visual processing attempt to form an efficient coding of ecologically valid stimuli. Using Independent Component Analysis to learn an efficient coding of stereoscopic images, we show that the ratio of binocular to monocular components varied with eccentricity at the same rate as human stereo acuity and Vernier acuity. Our results demonstrate that the organisation of the visual cortex is dependent on the underlying statistics of binocular scenes and, strikingly, that monocular acuity depends on the mechanisms by which the visual cortex processes binocular information. This result has important theoretical implications for understanding the encoding of visual information in the brain.

Effect of eccentricity on disparity distributions in binocular natural images

Sparse statistical representations of natural images produce energy efficient components resembling the responses of simple cells in V1. These models assume that the statistics are spatially stationary, when in fact these statistics are known to vary across the image. We analysed the effect of eccentricity on the independent components of binocular images. A set of binocular image pairs were divided into three regions according to distance from the focal point. For each region separate FastICA models were trained using 100,000 patches 2x25x25 pixels in size. 4000 components per region were generated in batches of 200. Pairs of Gabor functions were fitted to each component. The binocularity of components decreased with increasing eccentricity. The differences in position, phase and orientation within each pair were used as measures of disparity tuning. This was only done for clearly binocular components. Confidence intervals were calculated using 200 bootstraps. The spread of the distribution of preferred horizontal position disparity was greater for samples from more eccentric image locations. In contrast, the spread of preferred vertical position disparity was not affected by eccentricity. The distribution of phase disparities was markedly less symmetric towards the centre of the image compared with eccentric regions. More components were tuned to large phase disparities in the centre of the image than elsewhere. Orientation disparities showed a small but significant decrease with eccentricity. From the geometry of binocular vision the observed distributions of horizontal and vertical disparities is to be expected. The asymmetrical distribution of preferred phase disparities was not predicted, since the role of phase in disparity detection is not yet fully understood.

A genetic algorithm for face fitting

Accurate estimation of the shape of human faces has many applications from the movie industry to psychological research. One well known method is to fit a Three Dimensional Morphable Model to a target image. This method is attractive as the faces it constructs are already projected onto an orthogonal basis making further manipulation and analysis easier. To date use of Morphable Models have been limited by the inaccuracy and inconvenience of current face-fitting methods. We present a method based on Genetic Algorithms that avoid the local minima and gradient image errors that current methods suffer from. It has the added advantage of requiring no manual interaction to initialise or guide the fitting process.