A Refinement Method for Optimizing Emotion Recognition Vector Clustering
Year: 2025
Type: article
Abstract: Facial emotion recognition (FER) identifies human emotions through facial muscle movements, and research utilizing convolutional neural network (CNN) for FER has been actively conducted in recent years. However, CNN-based FER models are highly dependent on the quality of training data, which can be degraded by label inconsistency and inaccurate annotations. To mitigate these issues, a clustering-based approach is required to minimize human subjectivity and objectively interpret the intrinsic structure of the data. This study proposes an automated refinement pipeline that transforms high-dimensional emotion recognition vectors into a clustering-optimized representation. Experimental results using the FER-2013 dataset show that the refined data achieved a maximum clustering stability score of 0.92, while the unrefined data remained near zero, demonstrating the effectiveness of the proposed approach.
Authors: Shiu Kim, Moung-Ho Yi, Juhyun Shin
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7125112263
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