The Ideal Duration Of Smile Between Performer And Observer

Smile is meant to make people feel pleased to see you. Making a positive impression on others is dependent on the duration of smile. Hanibuchi et al. did a study to determine how observers perceive smile based on the duration of smiles. The smile patterns differed in duration between was fast (275 ms), normal (550 ms) and slow (1100 ms). 6 actors who can express smiles easily were selected for smiling for different duration. Actors as well as 20 other participants who were university and graduate students were observers for evaluating the experiment.

They found different results for actors and other participants who were not trained for smiling. Actors had more positive impressions when duration of smile was normal, which is 550 ms, and negative impressions in slow and fast conditions. While participants had more positive impressions when duration of smile was slow and fast. The duration for which smile should be given depends on who is on the receiving end. So make sure whether your crush is an actress or not before you give a smile to her.

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Reference - Shumpei Hanibuchi, Shogo Nishida and Kyoko Ito. Graduate School of Engineering Science Osaka University. 

Research paper name - A study of impression differences between smile performers and observers -Toward the Significance of Smile Training. 

Published in – IEEE (2010)

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Image courtesy of stockimages at FreeDigitalPhotos.net

This Study Shows How Distinguishable Smiles Are

Having emotions in robotics is advantageous in various ways. Giving a pleasant experience in human-machine interaction will be practicable. For service providers, smiling makes a huge difference in order to create a friendly environment. Incorporating smiles in robots will lead to better performance as virtual agents. This study helps in using smiling to enhance the rating of users to performance of robot or virtual agents.

Borutta et al. (2009) introduced a psychological system-theoretic approach to generate various types of smiles in robots artificially. They also described seven different types of smiles and discussed the hypothesis whether smiles are distinguishable.

A system-theoretic approach was based on the Zurich Model of Social Motivation, which describe the effect of smiling on emotional state of a human. Each of the type of smile among seven types of smile was based on one of the three state dimensions – security, arousal or autonomy.

Security – 1. Trustful Smile 2. Smile of Relief 3. Embarrassed Smile
Arousal – 4. Anxious Smile 5. Surprised Smile
Autonomy – 6. Superior Smile 7. Inferior Smile

Total 20 participants, 10 male and 10 female, took part in experiment in which 126 videos were shown to each participant with 18 videos of each type of smile. The experiment was conducted in two parts. In first part, participants answered this question – “What happens in this situation and how does the person feel?” In second part, participants answered this – “Which kind of smile is shown?” and had to answer among seven types of smile.

The results of first part show that fearful and embarrassed smiles were identified rarely. Inferior smile was mixed with dominance. Best results were obtained for trustful and surprised smiles. The results of second part show that there was a great variation in accuracy of classification by participants. Some of them had accuracy of 19%, while some of them achieved up to 50%.

The overall results show that the classification rate is higher for some types of smiles, especially trustful, surprised and inferior smile. In comparison, superior and embarrassed smiles were classified worst. The frequency response for trustful and anxious smile was similar, so they had been mixed up frequently. 

The perception of smiling as a positive emotion, weaknesses in animation and presenting some situations in fast motion to participants were major challenges before researchers. But, they were successful to generate seven different types of smiles and some of those types of smiles were distinguishable.

This study gave helpful insights about how to design emotions of robots or virtual agent based on ability of people to distinguish smiles.

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Reference - 

Researchers - Isabell Borutta , Stefan Sosnowski , Michael Zehetleitner, Norbert Bischof and Kolja Kuhnlenz

Research paper name - Generating Artificial Smile Variations Based on a Psychological System - Theoretic Approach

Published in: The 18th IEEE International Symposium on Robot and Human Interactive Communication

Toyama, Japan, Sept. 27-Oct. 2, 2009

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Image Courtesy - 

Trustful - Image courtesy of adamr at FreeDigitalPhotos.net

Surprised - Image courtesy of stockimages at FreeDigitalPhotos.net

Embarrassed - Image courtesy of stockimages at FreeDigitalPhotos.net

Anxious - Image courtesy of Gualberto107 at FreeDigitalPhotos.net

Automated System For Detecting Asymmetric Spontaneous Smiles

Asymmetric smiles show contempt, disapproval, doubt and defiance. When we see a guy not able to solve a simple mathematical equation or not aware in which foreign country the prime minister’s will be; only one corner of lip goes up while smiling on his lack of common knowledge. This asymmetry indicates a strong negative emotion.

To develop a system detecting spontaneous asymmetric smiles as people watched online videos, Senechal et al. did a study including one-sided and asymmetric facial expressions. The right facial nerve is independent of left facial nerve. When it comes to lower part of face, the muscles are contralateral which makes us easier to perform asymmetric expressions such as smirk. As muscles of upper face are bilateral, performing asymmetric movements is difficult. Try to raise only one eyebrow like the famous eyebrow-raising of ‘The Rock’.

Researchers collected and labeled 2265 videos of spontaneous data of clips ranging from 30 seconds to a minute. They also collected 200 posed videos in participants were asked to pose various asymmetric facial expression with slight variations like tilting their head. The template matching technique was used to identify asymmetric smiles in spontaneous data clips.

For each frame of video, the face was located, scaled, cropped and flipped around vertical axis to train and test. It was found that training with combination of mixed and spontaneous data gave best results.

After training and testing, they achieved 69% precision in detection which can be interpreted as only 1 false detection of asymmetric smile in every 85 videos. This system can be used for automatic detection of spontaneous asymmetric smiles. This provides valuable understanding of how people take part while watching videos or films. The emotions they convey while watching a fiction or real film gives insights about human nature through different aspects.

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Reference - 

Researchers - Thibaud Senechal, Jay Turcot and Rana el Kaliouby. Affectiva Inc., Waltham,

MA, USA.

Research paper name - Smile or Smirk? Automatic Detection of Spontaneous Asymmetric

Smiles to Understand Viewer Experience.

Published in: Automatic Face and Gesture Recognition (FG), 2013. 10th IEEE International Conference.