Categorizing facial expressions : a comparison of computational models

The original publication is available at www.springerlink.com Copyright SpringerRecognizing expressions is a key part of human social interaction, and processing of facial expression information is largely automatic for humans, but it is a non-trivial task for a computational system. The purpose of this work is to develop computational models capable of differentiating between a range of human facial expressions. Raw face images are examples of high-dimensional data, so here we use two dimensionality reduction techniques: principal component analysis and curvilinear component analysis. We also preprocess the images with a bank of Gabor filters, so that important features in the face images may be identified. Subsequently, the faces are classified using a support vector machine. We show that it is possible to differentiate faces with a prototypical expression from the neutral expression. Moreover, we can achieve this with data that has been massively reduced in size: in the best case the original images are reduced to just 5 components. We also investigate the effect size on face images, a concept which has not been reported previously on faces. This enables us to identify those areas of the face that are involved in the production of a facial expression.Peer reviewe

Spatial Stereoresolution for Depth Corrugations May Be Set in Primary Visual Cortex

Stereo “3D” depth perception requires the visual system to extract binocular disparities between the two eyes' images. Several current models of this process, based on the known physiology of primary visual cortex (V1), do this by computing a piecewise-frontoparallel local cross-correlation between the left and right eye's images. The size of the “window” within which detectors examine the local cross-correlation corresponds to the receptive field size of V1 neurons. This basic model has successfully captured many aspects of human depth perception. In particular, it accounts for the low human stereoresolution for sinusoidal depth corrugations, suggesting that the limit on stereoresolution may be set in primary visual cortex. An important feature of the model, reflecting a key property of V1 neurons, is that the initial disparity encoding is performed by detectors tuned to locally uniform patches of disparity. Such detectors respond better to square-wave depth corrugations, since these are locally flat, than to sinusoidal corrugations which are slanted almost everywhere. Consequently, for any given window size, current models predict better performance for square-wave disparity corrugations than for sine-wave corrugations at high amplitudes. We have recently shown that this prediction is not borne out: humans perform no better with square-wave than with sine-wave corrugations, even at high amplitudes. The failure of this prediction raised the question of whether stereoresolution may actually be set at later stages of cortical processing, perhaps involving neurons tuned to disparity slant or curvature. Here we extend the local cross-correlation model to include existing physiological and psychophysical evidence indicating that larger disparities are detected by neurons with larger receptive fields (a size/disparity correlation). We show that this simple modification succeeds in reconciling the model with human results, confirming that stereoresolution for disparity gratings may indeed be limited by the size of receptive fields in primary visual cortex

Temporal Integration of Movement: The Time-Course of Motion Streaks Revealed by Masking

Temporal integration in the visual system causes fast-moving objects to leave oriented ‘motion streaks’ in their wake, which could be used to facilitate motion direction perception. Temporal integration is thought to occur over 100 ms in early cortex, although this has never been tested for motion streaks. Here we compare the ability of fast-moving (‘streaky’) and slow-moving fields of dots to mask briefly flashed gratings either parallel or orthogonal to the motion trajectory. Gratings were presented at various asynchronies relative to motion onset (from to ms) to sample the time-course of the accumulating streaks. Predictions were that masking would be strongest for the fast parallel condition, and would be weak at early asynchronies and strengthen over time as integration rendered the translating dots more streaky and grating-like. The asynchrony where the masking function reached a plateau would correspond to the temporal integration period. As expected, fast-moving dots caused greater masking of parallel gratings than orthogonal gratings, and slow motion produced only modest masking of either grating orientation. Masking strength in the fast, parallel condition increased with time and reached a plateau after 77 ms, providing an estimate of the temporal integration period for mechanisms encoding motion streaks. Interestingly, the greater masking by fast motion of parallel compared with orthogonal gratings first reached significance at 48 ms before motion onset, indicating an effect of backward masking by motion streaks

Grasping isoluminant stimuli

We used a virtual reality setup to let participants grasp discs, which differed in luminance, chromaticity and size. Current theories on perception and action propose a division of labor in the brain into a color proficient perception pathway and a less color-capable action pathway. In this study, we addressed the question whether isoluminant stimuli, which provide only a chromatic but no luminance contrast for action planning, are harder to grasp than stimuli providing luminance contrast or both kinds of contrast. Although we found that grasps of isoluminant stimuli had a slightly steeper slope relating the maximum grip aperture to disc size, all other measures of grip quality were unaffected. Overall, our results do not support the view that isoluminance of stimulus and background impedes the planning of a grasping movement

Context Matters: The Illusive Simplicity of Macaque V1 Receptive Fields

Even in V1, where neurons have well characterized classical receptive fields (CRFs), it has been difficult to deduce which features of natural scenes stimuli they actually respond to. Forward models based upon CRF stimuli have had limited success in predicting the response of V1 neurons to natural scenes. As natural scenes exhibit complex spatial and temporal correlations, this could be due to surround effects that modulate the sensitivity of the CRF. Here, instead of attempting a forward model, we quantify the importance of the natural scenes surround for awake macaque monkeys by modeling it non-parametrically. We also quantify the influence of two forms of trial to trial variability. The first is related to the neuron’s own spike history. The second is related to ongoing mean field population activity reflected by the local field potential (LFP). We find that the surround produces strong temporal modulations in the firing rate that can be both suppressive and facilitative. Further, the LFP is found to induce a precise timing in spikes, which tend to be temporally localized on sharp LFP transients in the gamma frequency range. Using the pseudo R[superscript 2] as a measure of model fit, we find that during natural scene viewing the CRF dominates, accounting for 60% of the fit, but that taken collectively the surround, spike history and LFP are almost as important, accounting for 40%. However, overall only a small proportion of V1 spiking statistics could be explained (R[superscript 2]~5%), even when the full stimulus, spike history and LFP were taken into account. This suggests that under natural scene conditions, the dominant influence on V1 neurons is not the stimulus, nor the mean field dynamics of the LFP, but the complex, incoherent dynamics of the network in which neurons are embedded.National Institutes of Health (U.S.) (K25 NS052422-02)National Institutes of Health (U.S.) (DP1 ODOO3646