Abstract-A face recognition system must recognize a face from a novel image despite the variations between images of the same face. A common approach to overcoming image variations because of changes in the illumination conditions is to use image representations that are relatively insensitive to these variations. Examples of such representations are edge maps, image intensity derivatives, and images convolved with 2D Gabor-like filters. Here we present an empirical study that evaluates the sensitivity of these representations to changes in illumination, as well as viewpoint and facial expression. Our findings indicated that none of the representations considered is sufficient by itself to overcome image variations because of a change in the direction of illumination. Similar results were obtained for changes due to viewpoint and expression. Image representations that emphasized the horizontal features were found to be less sensitive to changes in the direction of illumination. However, systems based only on such representations failed to recognize up to 20 percent of the faces in our database. Humans performed considerably better under the same conditions. We discuss possible reasons for this superioriority and alternative methods for overcoming illumination effects in recognition.

Training was found to improve the performance of humans on a variety of visual perceptual tasks. However, the ability to detect small changes in the contrast of simple visual stimuli could not be improved by repetition. Here we show that the performance of this basic task could be modified after the discrimination of the stimulus contrast was practised in the presence of similar laterally placed stimuli, suggesting a change in the local neuronal circuit involved in the task. On the basis of a combination of hebbian and anti-hebbian synaptic learning rules compatible with our results, we propose a mechanism of plasticity in the visual cortex that is enabled by a change in the context.

Microsaccades are small rapid and involuntary eye movements that occur during fixation in an apparently stochastic manner. They are known to be inhibited in response to sensory transients, with a time course that depends on the stimulus parameters and attention. However, the temporal precision of their onsets and the degree to which they can be used to assess the response of the visual system to basic stimulus parameters is currently unknown. Here we studied microsaccade response properties as a function of the contrast and spatial frequency of visual onsets. Observers (n = 18) viewed and silently counted 2-min sequences of Gabor patches presented briefly (100 ms) at 1 Hz. Contrast and spatial frequency were randomized in different experiments. We found that the microsaccade response time, as measured by the latency of the first microsaccade relative to stimulus onset following its release from inhibition, was sensitive to the contrast and spatial frequency of the stimulus and could be used to extract a contrast response function without the observers' response. We also found that contrast detection thresholds, measured behaviorally for different spatial frequencies, were highly and positively correlated (R = 0.87) with the microsaccade response time measured at high contrast (>4 times the threshold). These results show that different measures of microsaccade inhibition, especially the microsaccade response time, can provide accurate and involuntary measures of low-level visual properties such as contrast response and sensitivity.

At early stages in visual processing cells respond to local stimuli with specific features such as orientation and spatial frequency. Although the receptive fields of these cells have been thought to be local and independent, recent physiological and psychophysical evidence has accumulated, indicating that the cells participate in a rich network of local connections. Thus, these local processing units can

Children with autism spectrum disorder (ASD) who can speak often exhibit abnormal voice quality and speech prosody, but the exact nature and underlying mechanisms of these abnormalities, as well as their diagnostic power are currently unknown. Here we quantified speech abnormalities in terms of the properties of the long-term average spectrum (LTAS) and pitch variability in speech samples of 83 children (41 with ASD, 42 controls) ages 4–6.5 years, recorded while they named a sequence of daily life pictures for 60 s. We found a significant difference in the group's average spectra, with ASD spectra being shallower and exhibiting less harmonic structure. Contrary to the common impression of monotonic speech in autism, the ASD children had a significantly larger pitch range and variability across time. A measure of this variability, optimally tuned for the sample, yielded 86% success (90% specificity, 80% sensitivity) in classifying ASD in the sample. These results indicate that speech abnormalities in ASD are reflected in its spectral content and pitch variability. This variability could imply abnormal processing of auditory feedback or elevated noise and instability in the mechanisms that control pitch. The current results are a first step toward developing speech spectrum-based bio-markers for early diagnosis of ASD.