Emotion Recognition Using Artificial Intelligence: A Review Of Facial, Speech And Multimodal Affective Computing

Emotion recognition enables computers to infer human emotional states such as happiness, sadness, anger, fear, surprise and neutrality from facial expressions, voice and other behavioural signals. It underpins affective computing applications in healthcare, education, customer service, driver monitoring and human–computer interaction. This paper reviews AI-based emotion recognition. It introduces categorical and dimensional models of emotion, then surveys facial expression recognition from hand-crafted features to deep convolutional networks trained on laboratory and in-the-wild datasets, speech emotion recognition from acoustic features and deep models, and multimodal fusion that combines face, voice and text. Benchmark datasets including CK+, FER-2013, AffectNet and RAVDESS are discussed. Scientific and ethical debates are examined, particularly evidence that facial movements do not map reliably onto inner emotions across people and contexts, along with privacy and consent concerns. A five-layer architecture comprising input capture, preprocessing, feature learning, fusion and classification, and application with feedback is proposed, together with research gaps, objectives and a comparison of modalities. The review concludes that emotion recognition can make interaction more natural when used transparently, with consent and with awareness of its limits.