Emotion detection is the process of recognizing or identifying different human emotions to include happiness, sadness, surprise, disgust, fear, anger, neutral, and more.
With changes in the emotional state, a person’s body-language changes altogether. There are visible changes in facial expressions, speech, gestures, movements, etc. These parameters or body language traits are leveraged for automatic emotion detection by machine learning.
How Does ML Help in Emotion Detection?
ML-based applications can detect emotions by learning what these body language traits (facial features, speech features, biosignals, posture, body gestures/movement, etc.) mean and apply this knowledge to the new set of data and information provided. This is how machine learning helps in emotion detection!
Today, advanced ML algorithms are available that can extract and leverage facial landmarks, voice features/activities, biosignals, body gestures/movements, motor behavioral patterns, and more, to detect emotions from various forms of data such as images, videos, etc. Using the right ML algorithm as per the purpose and needs can help get accurate results in emotion detection.
According to Markets and Markets, the global emotion detection and recognition market size is projected to grow to USD 56 billion by 2024 (Source).
Emotion detection has a remarkable contribution in various industries to include healthcare, marketing, entertainment, surveillance, retail, e-commerce, HR, and more. Emotion recognition is playing a significant role in marketing. It helps brands gauge user sentiments and user reactions to their products.
For example, in customer service, emotion detection using ML algorithms enables the identification of angry customers and helps businesses solve their issues on priority. Besides, AI and ML help redirect such customer’s query to the selected agent who is adept at handling the same.
ML process for emotion detection has multiple stages to include information (image, video, audio data) input/sequencing, image pre-processing, feature extraction using ML algorithms, removal of unwanted features, and classification using ML algorithms.
Ways of Emotion Detection Using Machine Learning
There are different ways or methods of approaching emotion detection or recognition through ML. Let’s see the popular ways here.
1. Facial Recognition
ML-based facial recognition is a commonly used method for emotion detection. It utilizes the fact that our facial features undergo significant changes with emotions. For example, when we are happy, our lips stretch upwards from both ends. Similarly, when we are excited, our eyebrows get raised.
Facial Recognition is a useful emotion detection technique in which through the identification of facial landmarks and using ML and deep learning, pixels of important facial regions are analyzed to classify facial expressions. Major facial landmarks used in emotion detection through ML are features to include eyes, nose, lips, jaw, eyebrows, mouth (open/close), and more.
While a particular facial landmark can appear similar in two different emotions, a careful analysis of the combination of different landmarks through AI and ML can help differentiate between similar-appearing but distinct emotions too. For example, while raised eyebrows can be a determinant of surprise, the same is also indicative of fear. But, raised eyebrows with lip boundaries raised upwards would indicate a pleasant surprise and not fear.
A complex example is “disgust”, identified through a combination of multiple facial feature changes to include eyebrows pulled down, nose wrinkled, upper lip pulled up, and lips loose. Emotion detection through facial recognition can find use in surveillance, healthcare, and more.
2. Speech Recognition
Speech recognition for emotion detection involves speech feature extraction and voice activity detection. The process involves using ML for analyzing speech features to include tone, energy, pitch, formant frequency, etc. and identifying emotions through changes in these.
ML-based emotion detection through speech or speech emotion recognition (SER) is popular because speech signals can be acquired conveniently and economically.
Speech emotion recognition using ML requires a good speech database, effective feature extraction, and the use of reliable classifiers using ML algorithms and natural language processing (NLP).
For accurate results, feature extraction and feature selection both are important. Then, classification of raw data into a particular class of emotion on the basis of features extracted from the data is done through various classification algorithms available to include Gaussian Mixture Model (GMM), Hidden Markov Model (HMM), Support Vector Machine (SVM), Neural Networks (NN), Recurrent Neural Networks, etc.
Major application areas for SER are audio surveillance, e-learning, clinical studies, banking, entertainment, call-centers, gaming, and many more. For example, emotion detection in e-learning helps understand students’ emotions and modify the teaching techniques accordingly.
Emotion detection through biosignals is the process of analyzing biological changes occurring with emotion changes. Biosignals include heart rate, temperature, pulse, respiration, perspiration, skin conductivity, electrical impulses in the muscles, and brain activity. For example, a rapidly increasing heart rate indicates a state of stress or anxiety.
These biosignals or physiological signals help get insight into the physiological states in humans. The challenge here is that a single biosignal is not enough because it can indicate multiple emotional possibilities. So, multiple biosignals from different parts of the body are used and the combinations of these are collectively analyzed. These biosignals (combinations) are then classified using ML techniques to include convolutional neural network (CNN) and more, and classification algorithms to include regression tree, support vector machine, linear discriminant analysis, Naive Bayes, etc.
This method is convenient as it is easy to record and analyze these biosignals through smart wearable devices today. And, for healthcare purposes, more complex biosignals are also recorded through electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG).
4. Body Gestures and Movements
Analyzing body movements and gestures also helps in emotion detection with the help of ML. Our body movements, posture, and gestures change significantly with changes in emotions. This is the reason why we can generally guess a person’s basic mood with a combination of his hand/arm gestures and body movements. For example, a clenched fist with an alert posture is a sign of anger. And, if a person is sad, he will generally have a dull posture.
Every emotion change in humans is accompanied by a series of gestures and body movement changes. Thus, studying a combination of multiple gestures and body movements can offer great insights into emotion detection with the help of appropriate ML classifier algorithms and gesture sensors like Microsoft Kinect, OpenKinect, and OpenNI.
The process of emotion detection through body gestures and movements involves the extraction of regions in relevant body parts, for example, from hands to get a hand region mask. Then, contour analysis is performed in this region that provides contours and convexity defects. This is used for classification. Five extended fingers imply open hands and no extended finger implies a fist.
5. Motor Behavioural Patterns
Using the right ML algorithms, the changes in behavioral patterns of a person with muscle tension, strength, coordination, and frequency also help define changes in the emotional state. These thus act as good parameters for emotion detection through machine learning. For example, symmetric up and down hand movements indicate a happy state.
This method leverages the fact that our body muscles react significantly to the changes in our emotional state, as a reflex action. While we would not even be aware of how prominent these changes are, these motor behavioral changes if recorded and analyzed properly through machine learning techniques, act as great indicators for emotion detection.
Ideally, a combination of two or more of these methods can offer the best results in emotion detection through ML. Machine learning improves with time and thus the results of these techniques are improving as the database is growing.
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