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. and solutions to measure user’s reaction automatically under such settings has remained a challenging task. Emotion Detection from facial expressions using AI can be a viable alternative to automatically measure consumer’s engagement with their content and brands.
At ParallelDots, we have combined the science of psychology, human expressions and artificial intelligence to recognize different emotions on an individual’s face automatically. 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. Using facial emotion detection smart cars can alert the driver when he is feeling drowsy.
The US Department of Transportation claims that driving-related errors cause around 95% of fatal road accidents. Facial Emotion Detection can find subtle changes in facial micro-expressions that precedes drowsiness and send personalized alerts to the driver asking him to stop for a coffee break, change music or temperature.
A candidate-interviewer interaction is susceptible to many categories of judgment and subjectivity. Such subjectivity makes it hard to determine whether candidate’s personality is a good fit for the job. Identifying what a candidate is trying to say is out of our hands because of the multiple layers of language interpretation, cognitive biases, and context that lie in between. That’s where AI comes in, which can measure candidate’s facial expressions to capture their moods and further assess their personality traits.
Notably, Unilever is already starting to incorporate this technology into their recruitment process. With this technology, a recruiter will be able to know, say, the overall confidence level of an interviewee and make a decision about whether or not this candidate will be able to perform well at a client-facing job. Similarly, it will be possible to find whether the candidate is honestly replying to all the questions by measuring the change in emotions during his responses and correlating it the vast amount of knowledge available in this area.
Employee morale can also be perceived using this technology by holding and recording interactions on the job. As an HR tool, it can help not only in devising recruiting strategies but also in designing HR policies that bring about best performance from employees.
Video games are designed keeping in mind a specific target audience. Each video game aims to evoke a particular behavior and set of emotions from the users. During the testing phase, users are asked to play the game for a given period and their feedback is incorporated to make the final product. Using facial emotion detection can aid in understanding which emotions a user is going through in real-time as he is playing without analyzing the complete video manually.
Such product feedback can be taken by analyzing a live feed of the user and detecting his facial emotions. While feelings of frustration and anger are commonly experienced in advanced video games, making use of facial emotion detection will help understand which emotions are experienced at what points in the game. It is also possible that some unexpected or undesirable emotions are observed during the game. Taking feedback from the user has experienced the game can be inefficient. This is because it can often be difficult to put an experience into words. Moreover, users may be unable to remember what exactly they went through emotionally across different parts of the game. Facial Emotion detection is a practical means of going beyond the spoken or written feedback and appreciating what the user is experiencing. When feedback is taken in this format, it becomes genuinely non-intrusive when it comes to user experience. At the same time, such feedback is more reliable than other forms.
Traditionally, market research companies have employed verbal methods such as surveys to find the consumers wants and needs. However, such methods assume that consumers can formulate their preferences verbally and the stated preferences correspond to future actions which may not always be right.
Another popular approach in market research industry is to employ behavioral methods that observe user’s reaction while interacting with a brand or product. Such methods are considered more objective than verbal methods. Behavioral methods use video feeds of users interacting with the product which are then analyzed manually to observer their reactions and emotions. However, such techniques can quickly become very labor intensive as the sample size increase. Facial Emotion Recognition can come to the rescue by allowing market research companies to measure moment-by-moment facial expressions of emotions (facial coding) automatically and aggregate the results.
Detecting emotions with technology is a challenging task, yet one where machine learning algorithms have shown great promise. Using ParallelDots’ Facial Emotion Detection API, customers can process images, and videos in real-time for monitoring video feeds or automating video analytics, thus saving costs and making life better for their users. The API is priced on a Pay-As-You-Go model allowing you to test out the technology before scaling up.
Check here to know more about different use cases of Virality Detection API which can be used to enhance image quality.
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