Evaluating Public Art in Commercial Complexes: A Dual-Channel Emotion Recognition Framework Fusing Facial Micro-Expressions and Semantic Analysis

Abstract

The development of public art in commercial spaces increasingly emphasizes emotional experience, but traditional evaluation methods struggle to objectively capture the real-time dynamics and inherent complexity of user emotions. This study proposes a dual-channel emotion recognition framework integrating facial micro-expression analysis and semantic understanding. We define seven emotional categories and construct a dataset of facial expressions and speech data. Feature-level fusion between DeepFace-processed facial data and DeepSeek-R1-analyzed semantic data generates a unified recognition model. An empirical study analyzing five types of public art media with 2,100 speech transcripts and 1,200 facial images reveals that dual-channel fusion achieves 93.2% accuracy, significantly outperforming single-modal approaches. Interactive art generates the strongest emotional stimulation, platform-based art enables efficient communication through social attributes, and functional art provides unique emotional buffering. This research offers a quantifiable methodology for commercial public art evaluation.

Keywords: Public Art, Affective Computing, Emotion Recognition, Micro-expression Recognition, User Environmental Experience, Commercial Architecture Complex

DOI: 10.54941/ahfe1007325