Emotive analytics is an important topic in the field of computer vision and artificial intelligence because of its academic and commercial potential. Its actually a blend of psychology and technology. It focuses its study on facial images, because visual expressions are one of the most important forms of interpersonal communication.
It uses emotion Recognition API for Analyzing Facial Expressions. Emotion recognition algorithm helps to find out emotion in a given photo or video. This is useful for many applications, like tagging social media images, marketing analysis, focus groups, security, sales training, or healthcare.
It includes sequence of images with micro facial expression as an input and locates feature reference points in alignment with a virtual face mesh and returns the confidence across a set of emotions in the image. In a video it extracts video and sends back those frames to the API. These API use facial detection, eye tracking, and specific facial position to determine a subject’s mood. This process is mainly divided into three steps. (1) Facial component detection, (2) Feature extraction, (3) Expression classification.
The emotions detected are anger, disgust, fear , happiness , sadness and surprise.
Its applications can be Fraud prevention, smart surveillance, criminal identification, advertising and healthcare etc. This technology seems to have a strong potential and is encouraging a seamless relationship between people, payments and possessions.