review · Neural Computing and Applications
Automated human emotion recognition applies machine learning to identify emotional states across diverse modalities. These methods analyse inputs including facial expressions, spoken words, written text, and biosignals such as electroencephalograms, blood volume pulses, and electrocardiograms. Systems can process these inputs individually or combine multiple data streams to achieve better recognition. Understanding human emotional states enables responsive technologies across sectors including marketing, digital gaming, educational platforms, and human to robot interaction. Historically, most developments have relied on controlled laboratory experiments and personalised models tailored to individual subjects. Recent progress focuses on moving beyond artificial conditions to test systems in real-world environments while building generic models capable of interpreting emotions across broader populations. An evaluation of current machine learning approaches outlines the technical landscape and highlights future research pathways required to mature automated emotion detection.
Emotion profoundly influences human reasoning, planning, and everyday decision making. Equipping computer systems with the capability to identify emotional states allows digital tools to interact with people more naturally. Responsive machines that perceive human sentiment can significantly improve services in education, interactive entertainment, and automated support systems by tailoring their actions to match the user's immediate emotional needs.
The abstract highlights practical use cases for emotion recognition across marketing, e-learning, video games, and human-robot interaction. However, commercial readiness remains at an early to intermediate stage: most existing systems rely on personalised models and controlled laboratory testing. Deployable commercial solutions require generic models capable of operating reliably in unconstrained, real-world environments.
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Abstract Emotion is an interdisciplinary research field investigated by many research areas such as psychology, philosophy, computing, and others. Emotions influence how we make decisions, plan, reason, and deal with various aspects. Automated human emotion recognition (AHER) is a critical research topic in Computer Science. It can be applied in many applications such as marketing, human–robot interaction, electronic games, E-learning, and many more. It is essential for any application requiring to know the emotional state of the person and act accordingly. The automated methods for recognizing emotions use many modalities such as facial expressions, written text, speech, and various biosignals such as the electroencephalograph, blood volume pulse, electrocardiogram, and others to recognize emotions. The signals can be used individually(uni-modal) or as a combination of more than one modality (multi-modal). Most of the work presented is in laboratory experiments and personalized models. Recent research is concerned about in the wild experiments and creating generic models. This study presents a comprehensive review and an evaluation of the state-of-the-art methods for AHER employing machine learning from a computer science perspective and directions for future research work.
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DOI: 10.1007/s00521-024-09426-2
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