Work

Artificial Intelligence-Based Decoding of Animal Micro-Expressions: A Review of Methodological Advances and Translational Applications

Year: 2026

Type: article

Abstract: Animal micro-expressions constitute transient behavioral windows that link internal states to externally observable signals, while artificial intelligence (AI) serves as the critical bridge that transforms these windows into measurable, interpretable, and applicable scientific tools. Rather than imposing a human-centered lexicon of expressions, AI-driven decoding aims to develop biologically grounded and increasingly comparable behavioral biomarkers that link computable facial dynamics to internal states; however, a validated universal cross-species framework has not yet been established. This review summarizes the common behavioral characteristics of animal micro-expressions, their cross-species expressive forms, and functional differences; systematically outlines the methodological spectrum through which AI captures, encodes, recognizes, and interprets these brief yet complex signals; and finally discusses the expanded applications of AI plus micro-expression analysis in basic research, clinical diagnosis, and animal welfare governance, thereby promoting a paradigm shift in the decoding of animal micro-expressions.

Source: Animals

DOI: 10.3390/ani16162578

Authors: Feng Su, Yangzhen Wang, Xiaying Li, Yusheng Wei, Yonglu Tian

Institutions: Peking University, Tsinghua University

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: National Natural Science Foundation of China, China Postdoctoral Science Foundation