How to create an Emotional Report?
This guide provides an overview of the emotional report building process, including a detailed outline and guidelines to write your own.
This article is describing our approach to assessing emotions from facial expressions in the context of watching a video
The article describes our approach to assessing emotions from facial expressions in the context of watching a video. The approach involves using a proprietary algorithm to compute the intensity of a smile, as a proxy for amusement; an eyebrow as a proxy for surprise and a frown, as a proxy for confusion. The algorithm uses a facemesh, which is a representation of 478 key points on the face, to analyze the movement and contraction of the facial muscles. The output is a score between 0.0 and 1.0 that reflects the intensity of the smile or frown. The approach is designed to be real-time and private, leveraging local processing power to analyze the key points of a facemesh.
Smiling is often associated with happiness and pleasure.
When a person smiles, it typically indicates that they are feeling positive emotions such as joy, amusement, or contentment. Smiling can also be a way of expressing friendliness or goodwill towards others.
However, it's important to note that smiling is a complex behavior that can have many different meanings and can be influenced by a variety of factors, including cultural and individual differences.
There is a foundational study from Paul Ekman that has investigated the relationship between smiling and emotions, in a paper from 1990 called: The Duchenne Smile Emotional Expression And Brain Physiology
A Duchenne smile is a genuine smile characterized by the activation of the muscles around the eyes, resulting in the creation of crow's feet wrinkles.
Some key points to know about a Duchenne smile are:
Amusement is a state of experiencing humorous and entertaining events or situations.
Our approach is to compute an evaluation of amusement based on smile detection involving several steps.
First, an image is captured using a traditional RGB or IR camera. This image is then passed through a face detection model that runs locally on the device. If a face is detected, a facemesh is estimated using the local processing power of the device. This facemesh is composed of 478 key points that represent the various landmarks on the face, such as the corners of the mouth, the eyebrows, and the nose.
Next, the stream of facemesh data is given as input to a proprietary algorithm that is specifically designed to estimate the intensity of a Duchenne smile. This algorithm uses the key points of the facemesh to analyze the movement and contraction of the facial muscles around the eyes and mouth, which are characteristic of a Duchenne smile. Based on this analysis, the algorithm computes a score between 0.0 and 1.0 that reflects the intensity of the Duchenne smile.
Overall, this approach allows for real-time and private by design evaluation of amusement based on smile detection, by leveraging local processing power and a specialized algorithm to analyze the key points of a facemesh.
Yes, frowning is often associated with negative emotions such as sadness, anger, or frustration.
When a person frowns, it typically indicates that they are feeling unhappy or displeased. Frowning can also be a way of expressing disapproval or dissatisfaction with something.
Like smiling, however, frowning is a complex behavior that can have many different meanings and can be influenced by a variety of factors.
Frowning can have several different meanings, depending on the context in which it is used. Some common meanings of frowning include:
Overall, the meaning of frowning can vary depending on the situation and the individual who is frowning. In general, however, frowning is often associated with negative emotions or feelings.
If someone frowns while watching a video, it could mean that they are not enjoying the video or that they are dissatisfied with it in some way. It is possible that the person is frowning because they find the content of the video to be boring or uninteresting, or because they are confused or frustrated by something that is happening in the video.
Alternatively, the person might be frowning because they are concentrating or thinking deeply about something that is happening in the video. For example, if the video is presenting information that is difficult to understand or that requires careful attention, the person might frown in order to focus their attention on the video.
Overall, the meaning of someone frowning while watching a video can vary depending on the context and the individual who is frowning. It is important to ask the person explicitly about their feelings in the context of the video, in order to determine the reason for their frown.
To disambiguate the meaning of a detected frown while watching a video, some potential survey questions could include:
Yes, there have been many studies that have investigated the relationship between frowning and emotions.
For example the study of Vaish et al. (2013), pointed out that:
Overall, the available evidence suggests that there is a strong connection between frowning and emotions.
Our approach to compute an evaluation of confusion based on frown detection involves several steps.
First, an image is captured using a traditional RGB or IR camera. This image is then passed through a face detection model that runs locally on the device. If a face is detected, a facemesh is estimated using the local processing power of the device. This facemesh is composed of 478 key points that represent the various landmarks on the face, such as the eyebrows, the forehead, and the corners of the mouth.
Next, the stream of facemesh data is given as input to a proprietary algorithm that is specifically designed to estimate the intensity of a frown. This algorithm uses the key points of the facemesh to analyze the movement and contraction of the facial muscles around the eyebrows and forehead, which are characteristic of a frown. Based on this analysis, the algorithm computes a score between 0.0 and 1.0 that reflects the intensity of the frown.
Overall, this approach allows for real-time and private by design evaluation of confusion based on frown detection, by leveraging local processing power and a specialized algorithm to analyze the key points of a facemesh.
Raising eyebrows is often associated with surprise or confusion.
When a person raises their eyebrows, it typically indicates that they are surprised or unsure about something. Raising eyebrows can also be a way of expressing skepticism or disbelief.
Like smiling and frowning, however, raising eyebrows is a complex behavior that can have many different meanings and can be influenced by a variety of factors.
Raising eyebrows can have a variety of meanings depending on the context and the individual who is raising their eyebrows. Some possible meanings of raising eyebrows include:
In their study Namba et al. (2021), conduct extensive research on distinct temporal features of genuine and deliberate facial expressions of surprise. These are the key findings:
Overall, the meaning of raising eyebrows can vary depending on the situation and the individual who is raising their eyebrows. In general, however, raising eyebrows is often associated with a feeling of surprise or uncertainty.
It is difficult to determine the exact meaning of someone raising their eyebrows while watching a video without further context or information. Raising eyebrows can indicate a variety of emotions or reactions, such as surprise, skepticism, curiosity, or disbelief. The meaning of the raised eyebrows would depend on the person's body language and other facial expressions, as well as the content of the video they are watching.
To disambiguate the meaning of an eyebrow raising while watching a video, we would need to consider the context in which the eyebrow raising occurred. This could include factors such as the person's facial expression and body language, the content of the video, and the person's verbal response to the video. By taking all of these factors into account, we may be able to determine the specific meaning of the eyebrow raising. For example, it may indicate surprise, skepticism, interest, or disbelief, depending on the situation.
Here are some possible survey questions that could help disambiguate the meaning of an eyebrow raising while watching a video:
Our approach to compute an evaluation of surprise, related to raising eyebrow detection, involves the use of a traditional RGB or IR camera to capture an image of the face.
This image is then applied to a face detection model, which is run locally on the device. If a face is detected in the image, a facemesh is estimated locally using keypoints on the face.
This facemesh is composed of 478 keypoints, which represent the various facial features, such as the eyes, nose, and mouth.
The stream of the facemesh is then given as input to our proprietary algorithm, which computes the facemesh input stream to estimate the intensity of an eyebrow raising.
The output of this algorithm is a value between 0.0 and 1.0, which indicates the intensity of the eyebrow raising. This value can be used to evaluate the level of surprise exhibited by the person in the image.
We have developed an approach for assessing emotions from facial expressions in the context of watching a video.
Our approach involves using a proprietary algorithm to compute the intensity of a smile, which we use as a proxy for amusement; a frown, which we use as a proxy for confusion and an eyebrow raising which we use as a proxy for surprise. The algorithm uses a facemesh, which is a representation of 478 key points on the face, to analyze the movement and contraction of the facial muscles. The output is a score between 0.0 and 1.0 that reflects the intensity of the smile, eyebrow raise or frown.
We note that it is important to ask questions in order to disambiguate confusion and surprise proxies. This approach allows for real-time and private emotion assessment.
This guide provides an overview of the emotional report building process, including a detailed outline and guidelines to write your own.
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