Smile intensity in photographs predicts divorce later in life

Motivation and Emotion (Impact Factor: 1.55). 06/2009; 33(2):99-105. DOI: 10.1007/s11031-009-9124-6

ABSTRACT Based on social–functional accounts of emotion, we conducted two studies examining whether the degree to which people smiled
in photographs predicts the likelihood of divorce. Along with other theorists, we posited that smiling behavior in photographs
is potentially indicative of underlying emotional dispositions that have direct and indirect life consequences. In the first
study, we examined participants’ positive expressive behavior in college yearbook photos and in Study 2 we examined a variety
of participants’ photos from childhood through early adulthood. In both studies, divorce was predicted by the degree to which
subjects smiled in their photos.

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Available from: Alissa M Butts, Sep 26, 2015
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    • "Other studies have used the intensity of facial expressions (e.g., in yearbook photos) to predict a number of social and health outcomes years later. For example, smile intensity in a posed photograph has been linked to later life satisfaction, marital status (i.e., likelihood of divorce ), and even years lived (Abel and Kruger, 2010, Harker and Keltner, 2001, Hertenstein et al., 2009, Oveis et al., 2009, Seder and Oishi, 2012). It is likely that research has only begun to scratch the surface of what might be learned from expressions' intensities. "
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    ABSTRACT: Both the occurrence and intensity of facial expressions are critical to what the face reveals. While much progress has been made towards the automatic detection of facial expression occurrence, controversy exists about how to estimate expression intensity. The most straight-forward approach is to train multiclass or regression models using intensity ground truth. However, collecting intensity ground truth is even more time consuming and expensive than collecting binary ground truth. As a shortcut, some researchers have proposed using the decision values of binary-trained maximum margin classifiers as a proxy for expression intensity. We provide empirical evidence that this heuristic is flawed in practice as well as in theory. Unfortunately, there are no shortcuts when it comes to estimating smile intensity: researchers must take the time to collect and train on intensity ground truth. However, if they do so, high reliability with expert human coders can be achieved. Intensity-trained multiclass and regression models outperformed binary-trained classifier decision values on smile intensity estimation across multiple databases and methods for feature extraction and dimensionality reduction. Multiclass models even outperformed binary-trained classifiers on smile occurrence detection.
    Pattern Recognition Letters 10/2014; DOI:10.1016/j.patrec.2014.10.004 · 1.55 Impact Factor
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    • "Psychological research has demonstrated that the presence or absence of emotion is an important way in which humans can gain information about each other and has important implications for our relationships [9] [10]. Psychologists have used the concept of empathy as the psychological mechanism by which we can detect the presence or absence of such emotions and make inferences about the target based on emotional information. "
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    ABSTRACT: Abstract. While the Turing test is regularly cited as a measure for determining the achievement of artificial intelligence, it has been suggested that the ability to demonstrate emotion, rather than advanced intellectual abilities, is more important in deceiving a judge regarding the humanity of an artificial agent. This paper examines two portrayals of artificially intelligent agents in the media – the replicants in Blade Runner and the androids (particularly Data) in Star Trek: The Next Generation. The replicants demonstrate emotions, while possessing a level of intelligence similar to humans. Data does not demonstrate emotion, but has intellectual abilities otherwise superior to humans. A sample of undergraduate students (N = 27) were provided with short excerpts of script dialogue between characters from both sources, with identifying information removed. Participants were asked to indicate the likely genus of the character, as well as deciding if the character was intelligent or not. In Blade Runner, the majority of participants could not indicate if they thought the characters were intelligent, but they mistook android characters for humans. On the other hand, Data was considered intelligent, but was not mistaken for human. Overall, participants were better at identifying the genus of human characters than that of androids.
    Turing Arts Symposium - AISB/IACAP World Congress 2012 - Alan Turing 2012; 07/2012
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