Affective Computing: How Does This Work?
Affective Computing in software solutions helps improve customer experience
Imagine you went to a store, saw an article, checked its price tag. You had mixed emotions. Well, now even in your wildest dreams, have you ever imagined a machine reading your extremely nuanced subtilties of human reactions. A machine is loaded with a specific software program that can predict whether a person is smiling out of frustration or shouting out of joy. Human-computer interaction in the recent times has gone beyond anyone’s imagination. Affective computing does this all.
Let’s have an insight:
What Is Affective Computing?
Affective Computing, is also called as Emotion AI, it is all about how AI can decode the emotional status of a human being by interpreting their facial expressions, such as head motion, facial distortions, movement of jaws and speech pattern etc. It detects, recognizes, and emulates human emotions through a programmed AI neural network. Without a doubt, humans can analyse and interpret complex emotional signals better. However, the gap is narrowing faster than we can all imagine, thanks to advancements in big data capability and its extremely powerful algorithms.
The Mechanism of Emotion AI:
The human face is complete canvas for communicating different emotional expressions. Whether it’s love, anger, joy, surprise, sadness, or fear, every human emotion sends facial signals whenever they occur. With the help of Affective Computing technology, these emotional signals can be deciphered way quicker and particular. Here is how the Emotion AI technology works:
- Firstly, the webcam captures micro-level facial expressions data, and this is placed in the RAM of the device.
- Then, the computing device gathers cues about the emotional status by analysing facial expressions, such as head postures, speech patterns, and eye movements.
- These images are lateranalysed using computer vision, big data and AI to decipher nine major traits such as age, gender, pose, face detector, emotions, arousal, valence, attention, wish, and other features.
- Deep learning technology constantly works in the backend to provide necessary predictive output.
- A specialist labels the emotions of a large number of images (and frame by frame in the case of videos), and the algorithm is trained to interpret this data accurately. The result obtained is compared with the manual labelling while taking errors into account.
Affective Computing in Different Fields:
- Online Education: Identifying the emotional response of a child while having a class would give profound impacts on changing the teaching and the conceptual landscapes of methodology. Emotion AI can give insights of the interest level of the students. Apart from this, it can also help universities and colleges to conduct online exams securely while controlling the incidences of cheating and fake attendance.
- HR and Recruitment: Do you know that global enterprises, such as Unilever, Dunkin Donuts, and IBM, have already started using emotion recognition technology for hiring candidates? As reported by Financial Times, Unilever reportedly saved more than 50,000 HR work hours in 2019. Recruiters are already using Emotion AI to handle large- scale recruitment drives with ease. Emotion AI allows them to evaluate from the entire pipeline of candidates and weed out unsuitable applicants in the early stage of the recruitment process, saving significant productive work hours.