Sentiment Analysis is already widely used by different companies to gauge consumer mood towards their product or brand in the digital world. However, in offline world users are also interacting with the brands and products in retail stores, showrooms, etc. Our facial emotion detection algorithm can identify seven different type of emotional states in real-time. In this post, we will discuss how such a technology can be used to solve a variety of real-world use-cases effectively. Car Manufacturers around the world are increasingly focusing on making cars more personal and safe for us to drive. In their pursuit to build more smart car features, it makes sense for makers to use AI to help them understand the human emotions.
Don’t look now: why you should be worried about machines reading your emotions
20+ Emotion Recognition APIs That Will Leave You Impressed, and Concerned | Nordic APIs |
Lauren Rhue does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment. Republish our articles for free, online or in print, under Creative Commons licence. Facial recognition technology has progressed to point where it now interprets emotions in facial expressions. This type of analysis is increasingly used in daily life. For example, companies can use facial recognition software to help with hiring decisions. Other programs scan the faces in crowds to identify threats to public safety.
Facial Expression Recognition
The Emotion Recognition Task measures the ability to identify six basic emotions in facial expressions along a continuum of expression magnitude. Computer-morphed images derived from the facial features of real individuals, each showing a specific emotion, are displayed on the screen, one at a time. Each face is displayed for ms and then immediately covered up to prevent residual processing of the image. The participant must select which emotion the face displayed from 6 options sadness, happiness, fear, anger, disgust or surprise. The outcome measures for ERT cover percentages and numbers correct or incorrect and overall response latencies, which can be looked at either across individual emotions or across all emotions at once.
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