AI-Based Emotion Recognition

Abstract

Behaviors, actions, pose, facial expressions, and speech are considered as channels that convey human emotions. Extensive research has been carried out to explore the relationships between these channels and emotions. The proposed method consists of a neural network-based solution combined with image processing and speech processing to classify the universal emotions: happy, anger, sad, and neutral. Speech processing includes extraction of spectral and temporal features like MFCC, energy, and then a set of values is given as input to the neural network. In image processing, Gabor filter texture features are used to extract a set of selected feature points. Mutual information is calculated and given as an input to the neural network for classification. The experimental results demonstrate the efficacy of audio-visual cues especially using few prominent features as overall accuracy of the combined approach is above 85%.

CNN Based Face Emotion Recognition System for Healthcare Application

“…This embedding can then be compared to others in the system's database for consistency. CNN-based facial recognition systems have applications in various industries such as social networking, advertising, security, and surveillance [5 However, facial recognition technology has become an issue of ethical privacy, especially due to potential bias and manipulation of technology [6,7]. important implications for potential applications in psychology, human-computer interaction, marketing, security, and in research, such as improving human-robot interaction, driver fatigue in vehicle insights, advertising and retail and improving the customer experience [8].…”