What is Emotion Recognition using Machine Learning?

Tudip

01 April 2019

Detecting human emotions with technology is almost a challenging task. Research has shown that 90% of our communication can be non-verbal. Now thanks to MACHINE LEARNING, there has never been a more exciting time in the history of computer science.

What is Emotion Recognition?

“Emotion recognition is a technique that allows reading the emotions on a human face using advanced image processing.”

Why is Emotion Recognition important?

In our daily life, we go through different situations and feelings about it. Emotion can be defined as a strong feeling about a human situation. These feelings and thoughts are expressed as facial expressions. The primary emotion levels are divided into six types: Love, Joy, Anger, Sadness, Fear, and Surprise.

Real life Emotion Recognition applications

Basically in companies to gauge consumer mood towards their product or brand. In health care using this technology, we can help patients necessity of medicine. In the automation sector, we can use this technology to understand human emotions. Using these technology cars can alert the driver when he is feeling drowsy. Imagine your car asking you to take a lunch break!

How do we map the expressions to emotions?

An expression can have either a positive or negative effect of emotion. The following table shows the relationship map between facial expressions and emotions.

It is very necessary to detect the correct emotion by using facial expressions:

 Emotion Increase the Likelihood Decrease the Likelihood
Joy Smile Brow Raise Brow furrow
Anger Brow Furrow Lid tighten Eye Widen Chin Raise Innerbrow Rise Brow rise smile
Disgust Nose Wrinkle Upper lips raise Lips Stuck Smile
Surprise Inner Brow Raise Brow Furrow Eye Widen Jaw Drop Brow Furrow
Fear Inner Brow Raise Brow Furrow Eye widen Lips Stretch Brow Raise Lip Corner Depressor Jaw Drop Smile
Sadness Inner Brow Raise Brow Furrow Lip Corner Depressor Brow Raise Eye Widen Lip Press Mouth Open Lip Stuck Smile

How it works

  1. Using Metrics:

    When a human shows a specific emotion or expression (e.g., a smile) along with the degree of confidence. By using a metrics it can be thought of as a detector: As the emotion or facial expression occurs and intensifies, the score rises from 0 (no expression) to 100 (expression fully present).\

  2. Using Datasets:

    The data subsists of 48×48 pixel grayscale images of faces. The faces have been automatically situated so that the face is more or less centered and occupies about the same amount of space in each image. The task is to divide in each face based on the emotion shown in the facial expression into one of seven categories (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral).

  3. Using ParralleDots:

    ParralleDots has created an AI-based solution that developers can use to recognize an image’s after being trained on a given data set. Some guidelines where we check out the AI analytics report here.

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