Contrast normalisation masks natural expression-related differences and artificially enhances the perceived salience of fear expressions

Fearful facial expressions tend to be more salient than other expressions. This threat bias is to some extent driven by simple low-level image properties, rather than the high-level emotion interpretation of stimuli. It might be expected therefore that different expressions will, on average, have different physical contrasts. However, studies tend to normalise stimuli for RMS contrast, potentially removing a naturally-occurring difference in salience. We assessed whether images of faces differ in both physical and apparent contrast across expressions. We measured physical RMS contrast and the Fourier amplitude spectra of 5 emotional expressions prior to contrast normalisation. We also measured expression-related differences in perceived contrast. Fear expressions have a steeper Fourier amplitude slope compared to neutral and angry expressions, and consistently significantly lower contrast compared to other faces. This effect is more pronounced at higher spatial frequencies. With the exception of stimuli containing only low spatial frequencies, fear expressions appeared higher in contrast than a physically matched reference. These findings suggest that contrast normalisation artificially boosts the perceived salience of fear expressions; an effect that may account for perceptual biases observed for spatially filtered fear expressions

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Differences in image properties across facial expressions: effects on perceived contrast, stimulus efficacy and oculomotor responses

Studies within visual psychophysics suggest that fearful facial expressions possess a special status within the visual system. This is evidenced across a range of experimental paradigms that together suggest perceptual biases for fearful expressions are driven by their low spatial frequency content. Unaddressed by this account, however, is the extent that expressions of fear compared to other faces differ in terms of the statistical image properties that define facial stimuli (their Fourier amplitude spectra), and the extent that such natural differences in image properties of expressions are influenced under experimental conditions. The present thesis contributes findings to the broader literature, demonstrating that fear expressions in particular are not naturally higher in stimulus properties known to influence salience. Broadband fear expressions instead contain lower levels of RMS contrast compared to other expressions, where this effect becomes most pronounced as their spatial frequency content increases. These expression-related differences in low-level image properties are discussed in relation to contrast normalisation and differences in perceived image contrast. Findings from Experiment 2 emphasise that facial stimuli normalised for physical contrast do not necessarily have the same perceived contrast, and that this depends on the contrast metric used to normalise faces. A fear advantage for perceived salience is significantly influenced by RMS contrast normalisation and high spatial frequency filtering. A behavioural investigation of Hedger, Adams and Garner (2015) shows that these same effects of contrast normalisation may, to some degree, inadvertently enhance the extent that fearful expressions exploit the contrast sensitivity function. Additionally, Experiments 4 and 5 explore these effects using continuous flash suppression (CFS) and saccadic latency (SL). Findings showed that CFS in particular is vulnerable to faces’ contrast content and spatial frequency content, and supports evidence of a high frequency-dependent bias for fear expressions. This contrasts with SL data where no such fear advantage, nor one that is influenced by contrast normalisation, is found when using a SL paradigm. Together, these findings are discussed in relation to the proposed perceptual bias for fearful face expressions, and the extent to which these reflect a genuine adaptive phenomena, or an inadvertent effect incurred from stimulus normalisation

Objective quality prediction of image retargeting algorithms

Quality assessment of image retargeting results is useful when comparing different methods. However, performing the necessary user studies is a long, cumbersome process. In this paper, we propose a simple yet efficient objective quality assessment method based on five key factors: i) preservation of salient regions; ii) analysis of the influence of artifacts; iii) preservation of the global structure of the image; iv) compliance with well-established aesthetics rules; and v) preservation of symmetry. Experiments on the RetargetMe benchmark, as well as a comprehensive additional user study, demonstrate that our proposed objective quality assessment method outperforms other existing metrics, while correlating better with human judgements. This makes our metric a good predictor of subjective preference

Saliency-based image enhancement

Ph.DDOCTOR OF PHILOSOPH