Emotion Detection

People are used to taking in visual cues such as facial expressions for reading the emotions of others. With the help of AI technology, emotion recognition models get even better at analyzing expressions on a human’s face and detecting their emotional states. The model identifies the following human emotions: neutral, joy, sadness, surprise, anger, fear, and disgust.

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Emotion detection

How Does Emotion Detection Work?

The model identifies all faces within an image; then, it takes each face as an input to analyze the facial expressions. The emotion detection system delivers the whole spectrum of emotions, their likelihoods and the main facial emotion.


The input is an image. 


The output is a collection of dictionaries with the coordinates of faces and detection probabilities. The position of the identified face is in the form of the top-left and bottom-right pixel coordinates of the frame. The value for the emotion (most likely emotion), the probability of the particular emotion, and a list of labels for all the sentiments and their likelihoods are presented in the related dictionary of a face.

Technologies Used in SmartClick’s Emotion Detection API

Pytorch logo

Code example


  "Probability": 0.9989516735076904,

Why Work with SmartClick?

Accurate Software

We offer full-scale software solutions that feature the highest accuracy and robustness and can support a wide range of applications.

Tailored Solutions

We develop our technologies in-house, which gives us the flexibility to customize our solutions according to the specific needs of our customers.

Experienced Team

We are a team of experienced and knowledgeable professionals who bring deep domain expertise to develop next-generation AI solutions.

Emotion Detection API Use Cases

Ad Testing

Our emotion recognition model may effectively be used in ad testing. By analyzing the facial emotions of ad viewers, the system delivers insights to help brands understand how their audiences are emotionally engaged when a brand is revealed in an advertisement. Companies can use these analytics to optimize the ad content and improve its performance.

Entertainment Content Analytics

With emotion detection, it is also possible to analyze entertainment content. Emotion recognition solutions provide insights into viewer emotional responses within movie trailers, films, streaming, TV, or other creative content, enabling brands to better understand audience reactions and identify the most emotionally engaging moments.

Surveillance Monitoring

Emotion detection can be applied to track people’s emotions for surveillance monitoring. The system may be used in various fields, e.g., in healthcare to monitor patients who show signs of stress and depression, in urban security to predict suspicious behaviors of people in public spaces, etc.


Rate Limit




20 requests per minute



25,000/month + $0.004 each other

60 requests per minute



100,000/month + $0.003 each other

80 requests per minute


Face-based emotion detection identifies human emotions based on facial expressions. The system analyses the changes in facial gestures and efficiently determines emotional states.

We use the PyTorch library for our emotion detection model.

The speed of our emotion recognition model is real time.

Yes, a neural network works to extract features from a person’s face image, and a classification model gives the probability of the person expressing one of the seven emotions.

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