An Application of K-Means and Cross-Correlation Techniques for Facial Emotion Recognition
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Abstract
This research proposes a high-performance, explainable, and person-specific architecture for Facial Emotion Recognition (FER) that addresses the computational complexity limitations commonly found in deep learning models. The proposed methodology is based on digital signal processing and leverages K-Means clustering to extract template vector features from critical areas of change. A matched filter set under a two-stage structure is then applied for emotion classification. Experiments conducted on the JAFFE dataset using cross-validation for targeted individuals demonstrate that the proposed architecture can perfectly classify all seven basic emotions with an accuracy of 100%. These results highlight the potential of the proposed approach in building highly accurate, lightweight, and user-adaptive emotion recognition systems.
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