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Face Reading AI

Everything about Face Reading AI

     

What is Face Reading?

Face Reading involves identifying the features and expressions on a human face. In a digital context, the term is often used synonymously with Face Analysis, Sentiment Analysis, Facial Expression Recognition, and Emotion Recognition. Using AI, computer software is able to read and analyze facial features. This forms the first step in facial recognition, and also enables the software to recognize human emotions in real time.

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How does Face Reading work?

With digital Face Reading, a camera is used to capture images that are transmitted to a Face Reading software application. The application then processes the data to detect faces and facial features (landmarks), and to identify expressions. The six basic expressions that can be detected are: happiness, sadness, anger, surprise, fear, and disgust.

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Face Reading technology is based on AI. Broadly speaking, AI algorithms learn to recognize facial features and expressions by training on large datasets and creating templates. When analyzing a particular face, Face Reading software compares the image of the particular face with the templates produced by the algorithms to identify facial features and to determine the predominant emotions.

When deep learning methods are used to train the AI algorithms used in Face Reading software, the accuracy of the software increases.

Why is Face Reading technology important?

Face Reading makes it possible to observe human emotions accurately and without bias. When conducting market research, for example, businesses are able to use Face Reading software to record and interpret the audience’s responses to products or advertisements. This helps to eliminate the inaccuracies that arise when users do not (or cannot) accurately express their feelings about a product or service in a survey or interview.

Usability research can also be conducted efficiently by using Face Reading technology. Instead of inviting users to a lab and physically observing them, researchers can observe users via a webcam that transmits live images to the Face Reading application.

Face Reading capability is also an essential part of facial recognition technology.

How is Face Reading applied in practice?

In addition to its use in market research and usability research, Face Reading is used by advertisers and retailers to measure the response to their digital campaigns in real time, and to make adjustments on the go to maximize return on investment.

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Retailers, for example, also use Face Reading to determine the predominant mood among shoppers at any given time. They then use this information to adapt the environment accordingly, for example by playing more suitable music or by changing the types of advertisements or notices that they display.

The automotive industry has also used Face Reading technology, in combination with other driver-assist functions, to wake drivers up if they begin to fall asleep behind the wheel.

In the entertainment industry, Face Reading technology can be used to record and analyze the audience’s responses to video  games, movies, and more. Entertainment companies can then use this data to produce more engaging content for their audiences.

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Technical Specifications

The table below shows how FaceMatch SDK performs on the Labelled Faces in the Wild (LFW) dataset:

FPRTPRThreshold (Inverse of distance)
0.10.99900 ±0.002130.55448
0.010.99667 ±0.005370.59791
0.0010.99367 ±0.006050.62989

FPR = False Positive Rate
TPR = True Positive Rate

These results are an indication only and are based on the specific dataset Labelled Faces in the Wild. Customers can expect similar performance, with possible variations due to hardware and the availability of annotated data.