Any Human Face

The story takes us back and forth between the past s, s and the present It switches focus between protagonists; Alex, Bruno, Andrew, Michel, Daniela, the mysterious Birdman, Martin, Sandro — sometimes alone, sometimes in fleeting pairs. We peer into their relationships, into moments of their lives, as though looking at old pictures, hearing a snippet of a memoir. All of the past, the back-story, moves forward, connecting as we know it must with the photographs and the exhibition and what it will provoke, none of it shining any light on the why of it all.

Interspersed are a few snippets about the girl. Kidnapped from a busy street in broad daylight, she is held below ground. Not tortured, not harmed, but frightened, and alone. Her episodes are undated. This current fashion for avoiding straightforwardly linear narrative is one that I, personally, am beginning to tire of. Few novelists pull it off with the necessary aplomb. I'm afraid that Lambert is not one of the few.

It serves to confuse, rather than build the tension. I keep having to check who is who, and when we are. Taking the girl out of context works well and shows how to use the device to seriously good effect — the remainder of the tale would have benefited from a purely chronological rendition. Setting this aside, however, what you have is an intrigue. Whether all of the issues are satisfactorily resolved by the penultimate pages might be a matter of opinion.

I wasn't so sure. The very last words, though, are by way of epilogue, or elegy, and suggest that if Lambert doesn't achieve his ambitions as a novelist, he should turn his pen to poetry. For more crime drama in the Italian capital and the precincts of the Holy City check out Michael Dibdin.

Normal Human Face Simulator

Joost has made enough money to retire somewhere exotic, leaving Andrew with the slowly failing venture. Andrew is a lonely man who is pretty disorganised, never getting around to sorting out the piles of stock everywhere, often giving books away or pretending he doesn't notice customers stealing them. Gradually we come to learn how the two men's stories are interlinked by a chain of circumstances and characters.

  • human face.
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What is in the bags, and how the contents are related to several crimes, gradually becomes apparent. There is no formal investigation, either by police or other characters. The crime that is the motive for other crimes unfolds in parallel with the story of the characters' lives. There are no heroics, solutions or shocking plot revelations.

Any Human Face

The equalized images were obtained using the equalize function in a commercial graphic software. It then redistributes regularly the intermediate pixel values of the distribution between these two extremes.

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Neural mechanisms of object recognition. From this database, we selected images that contained human faces, images that contained animals, and images that contained neither human faces nor animals. Evidence for parallel processing in feature and conjunction searches. View Original Download Slide. That is the official verdict anyway.

While performing one of the two tasks, half of the non-targets were targets of the other task, and the other half were neutral distractors. Note the variety of stimuli used in this experiment. The 24 adult volunteers in this study 12 women and 12 men; mean age 31 years, ranging from 19 to 53 years; 5 left-handed gave their informed written consent. All participants had normal or corrected-to-normal vision. To start a block of trials, they had to place their finger on a response pad for 1 s. A trial was organized as follows: Participants had to lift their finger as quickly and as accurately as possible go response each time a target was presented and to withhold their response no-go response when the photographs did not contain a target.

Responses were detected using infrared diodes. Subjects were given ms to respond; longer reaction times were considered no-go responses. This maximum response time delay was followed by a ms black screen, before the fixation point of the next trial was presented again for a variable duration, resulting in a random —ms intertrial interval. An experimental session included 16 blocks of 96 trials.

In 8 blocks, the target was an animal and in the remaining 8 blocks, the target was a human face. In each block, target and non-target trials were equally likely. Among the 48 non-targets, 24 contained targets of the other categorization task.

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Half of the subjects started with the animal categorization task, the other half with the human face categorization task and conditions alternated by blocks of two. Subjects had two training blocks of 48 images before starting the test session. Training pictures were not repeated during testing. Performance was evaluated by determining the percentage of correct trials and the latency at which subjects triggered their finger movement response, computed between stimulus onset and finger lift.

A Greenhouse-Geisser correction for nonsphericity was applied. From this database, we selected images that contained human faces, images that contained animals, and images that contained neither human faces nor animals. They were all horizontal photographs by pixels, sustaining a visual angle of about Animals included mammals, birds, fish, and reptiles.

Human faces were presented in real-world situations with views ranging from whole bodies at different scales to face close-ups and including Caucasian and non-Caucasian people. There was also a wide range of non-target images that included outdoor and indoor scenes, natural landscapes mountains, fields, forests, beaches, etc. Subjects had no a priori information about the presence, the size, the position, or the number of targets in an image.

Unique presentation of images prevented learning, and brief presentations prevented exploratory eye movements. In this section we will address three different aspects of processing: Overall, subjects were very accurate on both tasks, scoring ANOVA tests performed on the overall results revealed that subjects categorized human targets with a lower accuracy than animal targets There was no main effect of category on mean and median RT.

However, both measures presented a significant interaction between the category and orientation factors both: These main effects are explored in details in the two next sections using post hoc ANOVA, paired t tests, and Wilcoxon tests. Contextual faces versus animals: Here only the trials over 9, performed in each task with upright scenes are considered. Mean accuracy was virtually identical in the two tasks Reaction time RT distributions on correct and incorrect go-responses. RT distributions are presented with the number of responses expressed over time, with ms time bins.

Overall, no effect of the categorization task is seen on the early part of the RT distributions. Whether upright or inverted, responses to faces followed virtually the same time course as responses to animals A and B. Inversion slightly disrupted the processing time course of both target-categories C and D , an effect that was slightly more pronounced for faces. Time course of performance. Cumulative numbers of responses were used. By taking into account the hit and false alarm rates in a single value at each time point, this time course of performance gives an estimation of the processing dynamics for the entire subject population.

Confirming results from Figure 2 , performance time course functions were virtually identical for contextual human face and animal categories, independent of the orientation i. The inversion effect was very similar in both cases with a slightly earlier onset for human pictures. Accuracy, however, was biased differently in each of them. Regarding processing speed, upright contextual faces were not categorized faster than upright animals.

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Any Human Face has 52 ratings and 15 reviews. David said: [I'm bringing back this review in support of author Charles Lambert's new novel A View from t. Alex enjoys the attention of his latest lover. Bruno is generous with his money and his time; he lends Alex the flash car, dines him.

First, this was shown by the RT distributions of correct go-responses in both tasks Figure 2A. Second, there was no task effect on either mean ms in both conditions or median RT ms for faces and for animals Figure 2A and Figure 3A. Thus, on average, animals and faces were processed at the same speed according to mean and median RT. The analysis of these two factors confirmed that contextual faces and animals were categorized at the same speed within natural images. Comparing the time course performances of each task Figure 3A clearly shows that early responses were produced at similar latencies regardless of the task and that performances follow time courses that are virtually undistinguishable.

These early responses cannot be considered as anticipations because if behavior was random on target and distractor trials which are equally likely , hits and false alarms should have the same probability. The latency at which go-responses are statistically biased toward hits gives an indication of the minimal processing time required to trigger a motor response in the task while eliminating any bias due to anticipations.

The analyses were performed either on the overall data set by pulling together all trials from all subjects or for each subject separately. No significant differences between the contextual face and the animal categorization tasks were found. The minimal processing time was ms with the overall data set for both faces and animals and ms contextual faces versus ms animals for individual data.

These results do not support any processing speed advantage for human faces. Average Results From Experiment 1. The comparison of performance did not show any difference between the processing of contextual human faces and animals when presented in an upright orientation.

In our protocol, half of the stimuli were also presented upside down and the present section compares the processing of inverted contextual faces and inverted animals to investigate whether the similarity found with upright stimuli extends to inverted ones. As in the preceding section, the comparison is carried out on over 9, trials for each condition.

Mean accuracy was virtually identical for inverted faces Accuracy showed the same biases than with upright stimuli, with a higher accuracy Moreover, the higher accuracy on inverted distractors observed in the contextual face task Figure 4 illustrates the higher number of errors performed on inverted distractors in the animal task both when compared to the set of upright stimuli in the animal task and when compared to the set of inverted distractors processed in the contextual face task.

The figure also illustrates that, regardless of their orientation, neutral distractors induce a higher number of false alarms in the animal categorization task. Again this is true when compared to the other set of distractors in the animal task, or when compared to the performance on neutral distractors in the contextual face task. The data indicate a different processing of the distractors depending on the task performed by the subject. Statistically significant differences between two conditions are illustrated with an asterisk.

Comparison of incorrect go-responses triggered by neutral distractors nD in red and by distractors that were targets in the other categorization task tD in green. Comparison of incorrect go-responses triggered by upright UpD in orange and inverted InvD in blue distractors. When considering the average categorization speed, inverted faces were categorized about 10 ms slower than inverted animals.

How to Draw the Head from Any Angle

However, this processing speed difference failed to reach statistical significance for the minimal processing time as defined in the preceding section. Minimal RT was ms, regardless of the kind of targets to categorize, when calculated on the overall data set. Mean minimal RT calculated on all individual subject data was ms for animals and ms for faces.

The RT distributions and the performance time course functions for each task also show a good overlap of early responses regardless of the task. Differences are observed later around mean RT or for late responses. In this section, we focus more specifically on the presence and the strength of the inversion effect as a function of the target category. The percentage of correct go-responses decreased significantly with inversion for both animals In parallel with the slight decrease of global accuracy, inverted pictures were also categorized on average with significantly longer RT Figure 2C and 2D and Figure 3C and 3D than upright pictures mean RT: Although the global reaction time increase appears robust with both kinds of inverted targets at the level of mean and median RT, it is far from being as obvious when considering the minimal processing time.

When determined on the overall data, no effect was seen regardless of the categorization task. The time course of performance showed that the stimulus inversion did not simply shift the curve toward longer latencies but rather decreased the slope of the functions that originate at similar early latencies. Overall, subjects were able to respond both very accurately and rapidly in the two tasks. This level of performance is impressive given the extreme variability of the photographs used in this experiment. If this conclusion had already been reached from results of earlier studies, here we extend these findings by showing that 1 the fast coarse categorization of objects in natural scenes is very weakly affected by inversion; 2 contextual human faces cannot be processed faster or more efficiently than another relevant visual category such as animals; and 3 the inversion effect, although very weak in both tasks, is slightly more pronounced for faces.

We do not want to argue that this kind of rapid categorization process would apply to any object category; instead, it might depend on a certain level of expertise that needs to be determined beyond which the categorization of any behaviorally relevant object could rely on such fast processes.

Although we found evidence that inversion of natural scenes did produce reliable effects on performance, with responses delayed 13 ms vs. With such temporal constraints, very little time would be available to implement a mental rotation mechanism during the time course of the categorization process.

On the other hand, the speed of recognition of an object might depend on the rate of accumulation of activity from object selective neurons Perrett et al. Neurons in higher-level occipito-temporal visual areas respond to complex stimuli such as animals and faces. At the level of neuronal populations, the strength of the population response is correlated to the number of activated neurons. Through experience, more neurons, each one more selectively tuned, respond to animals, human faces, and body parts in the upright position compared to inverted positions.

Groups of neurons responding to upright and inverted objects would start to respond at about the same latency but responses would accumulate more slowly in the case of inverted stimuli, leading to an increase in response latency. This hypothesis is supported by the time course of performance Figure 3 that originated at similar latencies but increased with different slopes depending on whether the stimuli were presented upright or upside down. It follows that, on average, it takes slightly more time to reach the threshold for inverted stimuli, and therefore to categorize them. If the processing of upright faces and animals followed the same behavioral temporal course, what is special in faces that led to differences in the processing of inverted stimuli?

The inversion effect is usually taken as evidence that face processing relies preferentially on configural mechanisms distinct from part-based mechanisms thought to be more important in the processing of other objects e. When faces appear in their typical upright orientation, configural information is extracted. This extraction is disrupted by inversion, except for objects whose discrimination relies on characteristic features that are not affected by inversion.

Accordingly, neurons would fire less efficiently in response to inverted than upright faces, leading to a smaller accumulation of activity for inverted faces compared to inverted animals because the latter might be represented by a cell population less strictly tuned to the upright orientation. As a consequence, the stronger inversion effect for faces often explained by the specificity of face processing Farah et al.

However, it remains possible that different strategies or brain mechanisms were used in the two tasks. Inversion had different effects on each category: This could be the consequence of a greater similarity between animals and distractors than between faces and distractors, and the use of more specific representations to perform the face task than the animal task.

This hypothesis is supported by the fact that more errors on neutral distractors and on inverted distractors were performed during the animal task than during the face task. Finally, animals were slightly more easily detected in natural scenes than faces, which might indicate that the two sets of images were not equated in difficulty and might potentially have masked a processing speed advantage in favor of faces.

Furthermore, this discrepancy might also potentially explain the very weak inversion effect found for faces.

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To test these alternative explanations and further characterize the processing of faces in natural scenes, we designed a second experiment. Experiment 2 was designed to compare the rapid categorization of faces and animals with more homogenous sets of images.

In Experiment 2, subjects were only presented with close-up views of human and animal heads and were required to categorize human faces and animal faces. Except where otherwise mentioned, methods were identical to those used in Experiment 1. The 24 human participants 12 women and 12 men, mean age 30 years, ranging from 19 to 51 years, 3 left handed who volunteered in this study gave their informed written consent. Nine of them had participated in the first experiment.

An experimental session included 8 blocks of 96 trials. Subjects performed two categorization tasks: Among the 48 non-targets, 24 were targets in the other categorization task. Thus, when performing a human face categorization task on a 96 trial block, 48 pictures contained at least one human face, 24 non-target scenes contained animal faces, the last 24 non-targets being neutral distractors i. Half of the targets and half of each non-target subset were presented upright while the other half was presented inverted. The design was counterbalanced so that in the overall group of subject, each image was seen in upright and inverted positions and processed as a target and as a non-target.

Half of the subjects started with the animal face categorization, the other half with the human face categorization. Subjects had one training block before starting each of the two test sessions. Training pictures were not used during testing. Picture examples and experimental design. Nomenclature as in Figure 1. A total of photographs were selected from the Corel Stock Photo Library; contained human faces, additional images contained animal faces, and the last photographs contained neither human nor animal faces Figure 5. They were all horizontal photographs by pixels, sustaining about