01-18-2013, 03:33 AM
Staging of emotions to manage the thoughts of other people just got a lot harder. If you are not a professional thief or a manipulator you would probably not be worried about this news, but from time to time it is nice to remind how developed the technology is today, says SingularityHub.
The New Software
Scientists at MIT have developed a software that is able to distinguish between "real" smile that is a product of sincere excitement of the "fake" smile that occurs as a product of some frustration. Whether you realize it or not, scientists say that most people laugh when trying to deal with some frustration. The problem occurs when the non-trained human eye interprets this "frustrated" smile as a "real" smile of delight. What is actually the difference between these smiles?
Frustrated Smile Syndrome
By analyzing the video recordings, the researchers concluded that the fundamental difference is in duration: sincere smile develops gradually, and as a result of frustration smile appears suddenly and disappears in the same way from the face. During the research scientists have conducted a lot of experiments. With a sincere smile, computer people equally well estimated emotion that smile was showing. In "Frustrated" Smile Syndrome, people misinterpreted emotion in roughly half of the cases, while the software in 92% of cases identified the emotion behind the smile correctly.
The motive for conducting this research was to help the people who have problems with interpretation of direct personal communication (such as people with various forms of autistic disorder). In addition, scientists have developed new software and other practical functions.
Experimental Settings
This study, which was published in the journal IEEE Transactions on Affective Computing (unfortunately, the full version of the article requires registration, which is not free, but a summary of the article is available for free), is based on two separate experiments. In the first experiment, the scientists requested from the male and female respondents to express the delight and frustration. After this, the participants had to fill out an online questionnaire that was designed to create a natural reaction to the frustration (deliberately encouraging frustrating online experiences such as slow page load, CAPTCHA, the disabled and copy-and-paste option of transferring text). Just reading these brutal methods used in the experiment causes frustration and that was followed by second part of the experiment in which they attempted to boost enthusiasm reaction in participants (by playing the popular Youtube video baby laughing, etc.)
In both experiments, detailed webcam was recording the facial expressions of subjects. After that, the expressions were analyzed with the help of Google software for the analysis of facial expressions that captures 22 different points on the face and received data was inserted into the interpretive mathematical model which is able to distinguish true from false smiles.
The results of the study offered some pretty interesting results related to the human smile. For example, there is an obvious difference in the speed and size of different types of smiles. Smiles of delight lasted an average of 13.8 seconds, while smiles as an expression of frustration on average lasted about 7.5 seconds. Also, smiles of enthusiasm were 60% more intense than frustration smiles.
What is very interesting is that the majority of respondents were not even aware that they naturally smiled as a reaction to the feeling of frustration. It seems that the idea that someone is frustrated substantially limits the ability to remember of the smile in the particular situation as for most of the respondents, smile is just completely non related to the sense of frustration. This explains the fact that the majority of respondents misinterpreted smile that came as a result of frustration.
On the basis of the recorded data, the researchers ultimately concluded that the most useful component in determining the emotions behind the smile is dynamic form through which the smile develops over time. The very fact that the face is smiling, tells us virtually nothing about how a person feels.
What is truly fascinating about this study is that it provides a better understanding of human behavior, but also that it can be constructed by using a computer program that helps certain people in interpersonal communication.
The Possible Uses of New Software
This is primarily due to individuals who suffer from some form of autism. They usually have developed non-verbal communication skills, especially when they need to "read" other people's emotions. Autistic individuals, simply put, tend to “prove” the emotional state of the person by looking at the parts of the face where the emotions are not expressed. To assist them, the most common treatment is learning basic facial expressions and adopting some basic principles such as the smile that means that someone is happy, and so forth.
Clearly, this research may be the best proof of how wrong the basic principles of learning are, but still they are an integral part of therapy for people with autistic disorder. Therefore, such persons should probably learn to distinguish between different types of smiles but only to note the fact whether or not there is a smile. This research is a major step forward since it shows that it is possible to develop sophisticated software by which autistic people will be able to notice how quickly the smile is developing, how long it lasts, etc. This technology will likely be even more important in the future because the U.S. CDC (Center for Disease Control and Prevention) claims that 1 of 88 children suffer from an autistic disorder, but these figures are a clear upward trend.
Fine nuances to differentiate types of smiles on people show that this research can be used even for analyzing differences among cultures where a smile has an important role in communication.
Possible areas for application of the new software are quite a range. One of the areas are programs for the analysis of facial expressions that would, with the help of new software better read emotions. Analysis of expression is becoming more widespread and more advanced, and the results of this research could help to develop programs that would be able to notice the changes of behavior and subtle facial expressions that would be confusing to many people or they would even not be able to notice them at all. In addition to the algorithm for age determination which was recently presented by Face.com, it would be possible to create an algorithm to determine the emotions that would be of great help in police investigations. Another possible application are so-called new technologies. Augmented reality like Google Glass. Imagine that when you start a conversation with some (known or unknown) person you have a software that tells you in the emotional state of the person.
The New Software
Scientists at MIT have developed a software that is able to distinguish between "real" smile that is a product of sincere excitement of the "fake" smile that occurs as a product of some frustration. Whether you realize it or not, scientists say that most people laugh when trying to deal with some frustration. The problem occurs when the non-trained human eye interprets this "frustrated" smile as a "real" smile of delight. What is actually the difference between these smiles?
Frustrated Smile Syndrome
By analyzing the video recordings, the researchers concluded that the fundamental difference is in duration: sincere smile develops gradually, and as a result of frustration smile appears suddenly and disappears in the same way from the face. During the research scientists have conducted a lot of experiments. With a sincere smile, computer people equally well estimated emotion that smile was showing. In "Frustrated" Smile Syndrome, people misinterpreted emotion in roughly half of the cases, while the software in 92% of cases identified the emotion behind the smile correctly.
The motive for conducting this research was to help the people who have problems with interpretation of direct personal communication (such as people with various forms of autistic disorder). In addition, scientists have developed new software and other practical functions.
Experimental Settings
This study, which was published in the journal IEEE Transactions on Affective Computing (unfortunately, the full version of the article requires registration, which is not free, but a summary of the article is available for free), is based on two separate experiments. In the first experiment, the scientists requested from the male and female respondents to express the delight and frustration. After this, the participants had to fill out an online questionnaire that was designed to create a natural reaction to the frustration (deliberately encouraging frustrating online experiences such as slow page load, CAPTCHA, the disabled and copy-and-paste option of transferring text). Just reading these brutal methods used in the experiment causes frustration and that was followed by second part of the experiment in which they attempted to boost enthusiasm reaction in participants (by playing the popular Youtube video baby laughing, etc.)
In both experiments, detailed webcam was recording the facial expressions of subjects. After that, the expressions were analyzed with the help of Google software for the analysis of facial expressions that captures 22 different points on the face and received data was inserted into the interpretive mathematical model which is able to distinguish true from false smiles.
The results of the study offered some pretty interesting results related to the human smile. For example, there is an obvious difference in the speed and size of different types of smiles. Smiles of delight lasted an average of 13.8 seconds, while smiles as an expression of frustration on average lasted about 7.5 seconds. Also, smiles of enthusiasm were 60% more intense than frustration smiles.
What is very interesting is that the majority of respondents were not even aware that they naturally smiled as a reaction to the feeling of frustration. It seems that the idea that someone is frustrated substantially limits the ability to remember of the smile in the particular situation as for most of the respondents, smile is just completely non related to the sense of frustration. This explains the fact that the majority of respondents misinterpreted smile that came as a result of frustration.
On the basis of the recorded data, the researchers ultimately concluded that the most useful component in determining the emotions behind the smile is dynamic form through which the smile develops over time. The very fact that the face is smiling, tells us virtually nothing about how a person feels.
What is truly fascinating about this study is that it provides a better understanding of human behavior, but also that it can be constructed by using a computer program that helps certain people in interpersonal communication.
The Possible Uses of New Software
This is primarily due to individuals who suffer from some form of autism. They usually have developed non-verbal communication skills, especially when they need to "read" other people's emotions. Autistic individuals, simply put, tend to “prove” the emotional state of the person by looking at the parts of the face where the emotions are not expressed. To assist them, the most common treatment is learning basic facial expressions and adopting some basic principles such as the smile that means that someone is happy, and so forth.
Clearly, this research may be the best proof of how wrong the basic principles of learning are, but still they are an integral part of therapy for people with autistic disorder. Therefore, such persons should probably learn to distinguish between different types of smiles but only to note the fact whether or not there is a smile. This research is a major step forward since it shows that it is possible to develop sophisticated software by which autistic people will be able to notice how quickly the smile is developing, how long it lasts, etc. This technology will likely be even more important in the future because the U.S. CDC (Center for Disease Control and Prevention) claims that 1 of 88 children suffer from an autistic disorder, but these figures are a clear upward trend.
Fine nuances to differentiate types of smiles on people show that this research can be used even for analyzing differences among cultures where a smile has an important role in communication.
Possible areas for application of the new software are quite a range. One of the areas are programs for the analysis of facial expressions that would, with the help of new software better read emotions. Analysis of expression is becoming more widespread and more advanced, and the results of this research could help to develop programs that would be able to notice the changes of behavior and subtle facial expressions that would be confusing to many people or they would even not be able to notice them at all. In addition to the algorithm for age determination which was recently presented by Face.com, it would be possible to create an algorithm to determine the emotions that would be of great help in police investigations. Another possible application are so-called new technologies. Augmented reality like Google Glass. Imagine that when you start a conversation with some (known or unknown) person you have a software that tells you in the emotional state of the person.
![[+]](https://www.biotechnologyforums.com/images/netpen-pro/collapse_collapsed.png)