What is Emotion Recognition using Machine Learning?

Baichitrananda Choudhury

Baichitrananda Choudhury

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:

 EmotionIncrease the LikelihoodDecrease the Likelihood
JoySmileBrow Raise Brow furrow
AngerBrow Furrow Lid tighten Eye Widen Chin RaiseInnerbrow Rise Brow rise smile
DisgustNose Wrinkle Upper lips raiseLips Stuck Smile
SurpriseInner Brow Raise Brow Furrow Eye Widen Jaw DropBrow Furrow
FearInner Brow Raise Brow Furrow Eye widen Lips StretchBrow Raise Lip Corner Depressor Jaw Drop Smile
SadnessInner Brow Raise Brow Furrow Lip Corner DepressorBrow 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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