Emotion Analysis Recognition Software
What is Emotion Recognition?
Emotion recognition is the process of detecting displayed human emotions using artificial intelligence based technologies in order to evaluate non-verbal responses to products, services or goods. Emotion analysis can be carried out using face detection or facial recognition technology which enables programs to “examine” the sentiments on a human face by utilizing sophisticated image dispensation. Facial expressions are generally analyzed by using cameras to detect faces and capture real-time human responses to real-world scenarios. Each facial expression that an individual displays causes the facial muscles to move and contract differently, and this makes the process of determining an emotion easier for the deep learning AI algorithms.
What universal emotions can be detected and how are they recognized?
There are seven recognized universal emotions according to psychologist Dr. Paul Ekman. Below are the seven emotions and the commonly associated facial characteristics (facial expressions) that come with them. These characteristics are learned by deep learning, emotion detection technologies through large quantities of image training data.
Happiness – Raised eyebrows and corners of the mouth
Surprise – Raised eyebrows but not drawn together, raised upper eyelids, jaw dropped down
Anger – Eyebrows pulled down, eyes open wide, lips pursed tightly together
Fear – Jaw dropped open, lips pressed backwards horizontally, upper eyelids raised
Sadness – frowned smile with lip corners pulled down, inner corners of the eyebrows pulled together, eyelids drooped.
Disgust – Upper lip raised in a ‘u’ like shape, eyebrows lowered, wrinkling of the nose
Contempt – Raised lip corner on one side of the face, this is the only signifier but it can also be accompanied by a smile or angry expression.
Sightcorp’s deep learning based, emotion analysis product DeepSight, can detect seven universal emotions anonymously and in real-time. It is trained using diverse data sets that help to make facial expression analysis more accurate. When an emotion is detected a confidence score is provided, alongside a timestamp and the data is then aggregated and can be viewed on any data visualization dashboard for ease and convenience.
How can emotion recognition be employed?
Emotion recognition is already widely used by different companies to gauge consumer mood towards their products, brands, marketing efforts, staff or in-location experiences. Understanding customer emotions is vital to ensure business growth and enhance experiences, however the opportunities brought by this technology goes further than market research and digital advertising.
Emotion recognition in for online admissions and interviews:
Emotion recognition can be used to understand how candidates feel during interviews and to measure how they react to certain questions. This information can be used to optimize interview structure for future candidates and streamline the application process. Using Sightcorps technology you can also measure attention using head orientation/pose analysis.
Emotion Analysis for A/B campaign testing:
Using emotion analysis technology it is possible to pre-evaluate remotely or on site marketing campaigns, This helps to ensure that they are evoking the right reactions before launching to the market. Using facial expression recognition software, advertisers can see which ads are receiving high engagement and positive emotional responses from viewers and provides them with the tools to run tests at scale on different target audiences to ensure that the campaigns with the highest impact are selected.
Emotion analysis for online education:
Anonymous emotion detection for online education is an ideal way to analyze the online student journey and improve it where necessary. Assess schools course materials, teaching styles, structure and layout by way of emotional feedback as student’s go through each module in real-time. Use true facial responses and engagement levels to find points of interest or course stumbling blocks and make optimizations.
Emotion recognition in health care:
An industry that’s taking advantage of this technology is health care, with AI-powered recognition software helping to decide when patients need medicine, assess their emotional response in clinical trials or to help physicians in deciding how to best triage their patients.
Emotion analysis in video game testing:
Video games are designed with a specific target audience in mind and aim 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 recognition can aid in understanding which emotions a user is experiencing in real-time. This is a great addition to verbal feedback as it provides a more complex review of the gaming experience.
Automotive industry and emotion analysis:
The automotive industry is also applying emotion recognition technology, as car manufacturers around the world are increasingly focusing on making cars more personal and safe for people to drive. In their pursuit to build smart car features, it makes sense that car manufacturers use AI to help them understand human emotions. Using facial emotion detection smart cars can alert the driver when he is feeling drowsy and in turn help to decrease road casualties.
What are the advantages of facial expression recognition?
Detecting emotions with technology was once a quite challenging task, yet one where deep learning algorithms have shown great promise. By using Facial Emotion Recognition, businesses 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.
At Sightcorp, we have combined the science of psychology, human expressions and artificial intelligence to recognize different universal emotions on an individual’s face automatically. Our face analysis software can identify up to seven different types of emotional states in real-time and does so anonymously, ensuring that the privacy of individuals is protected at all times.
