Emotion Detection

 

I CAN SEE YOU: 
How to establish someone’s feelings using facial and emotion recognition

Face Detection

Face detection, often known as facial recognition, is a computer technique based on artificial intelligence that locates and recognizes human faces in digital photos and videos. Real-time tracking and surveillance of individuals is a common use of face detection technology. Security, biometrics, law enforcement, entertainment, and social media are just a few of the industries that employ it.

Facial detection makes use of artificial neural network (ANNs) and machine learning (MLs) algorithms. Face detection in face analysis uses facial expressions to pinpoint the areas of a picture or video that need to be focused on in order to extract information about age, gender, and emotions. To create a faceprint and compare it to previously saved faceprints, a facial recognition system needs a lot of data.

How does face detection works?

Face recognition apps combine AI, machine learning, statistical analysis, and image processing to identify human faces in photos and separate them from non-human things like landscapes, buildings, and body parts. Before making the face detection, the evaluated material is preprocessed to increase quality and remove any potential interference.

Face detection algorithms usually begin by looking for human eyes, which are one of the easiest traits to recognize. They next attempt to identify face features like brows, lips, nose, nostrils, and irises. When the algorithm determines that it has discovered a facial area, it does further tests to ensure that it has spotted a face.

For maximum accuracy, the algorithms are trained using massive data sets containing hundreds of thousands of positive and negative pictures. The training increases the algorithms' capacity to detect faces in images and determine their precise locations.

Applications for face detection: surveillance, identify crimes, security, mobile applications, airports.

Region of Interest (ROI)

The main ability of Region of Interest (ROI) in the context of computer vision and image processing is its capability to focus on specific areas within an image or a frame of video. ROI allows you to define and extract a particular region or regions of interest from the overall visual data.

Emotion Detection

In the fields of computer vision and artificial intelligence, emotion detection—also referred to as facial emotion recognition or facial expression analysis—focuses on recognizing and decoding human emotions from facial expressions. The objective is to create algorithms and systems that can analyze face characteristics to identify and classify people's emotional states.

These feelings, which are portrayed through certain facial expressions and are sometimes referred to as the "basic emotions" or "primary emotions," are thought to be essential to the human experience. The widely recognized basic emotions include:

1. Happiness
2. Sadness
3. Anger
4. Surprise
5. Neutral
6. Fear
7. Disgust

Emotion detection through voice

Speech Emotion Recognition is a task of speech processing and computational paralinguistics that aims to recognize and categorize the emotions expressed in spoken language. The goal is to determine the emotional state of a speaker from their speech patterns, such as prosody, pitch, and rhythm. The process typically involves extracting features from speech signals and utilizing machine learning techniques to classify and identify emotions. Acoustic features such as pitch, intensity, and speech rate, as well as linguistic features like prosody and spectral content, are commonly used to capture emotional cues embedded in speech. These features are then fed into various classification algorithms to accurately identify the emotional state of the speaker. If combined with the facial emotion detection, the model could get stronger and could validate the emotion of a person considering both characteristics.

Applications for emotion detection through face, body gesture or speech: health care, marketing, drivers tracking, simulations.

References

[1] https://arxiv.org/ftp/arxiv/papers/2006/2006.04057.pdf

[2] https://paperswithcode.com/task/speech-emotion-recognition

[3] https://www.techtarget.com/searchenterpriseai/definition/face-detection