Unsupervised Clustering and Ensemble Learning for Classifying Lip Articulation in Fingerspelling


Amangeldy N. Gazizova N. Milosz M. Kurmetbek B. Nazyrova A. Kassymova A.
June 2025Multidisciplinary Digital Publishing Institute (MDPI)

Sensors
2025#25Issue 12

This paper presents a new methodology for analyzing lip articulation during fingerspelling aimed at extracting robust visual patterns that can overcome the inherent ambiguity and variability of lip shape. The proposed approach is based on unsupervised clustering of lip movement trajectories to identify consistent articulatory patterns across different time profiles. The methodology is not limited to using a single model. Still, it includes the exploration of varying cluster configurations and an assessment of their robustness, as well as a detailed analysis of the correspondence between individual alphabet letters and specific clusters. In contrast to direct classification based on raw visual features, this approach pre-tests clustered representations using a model-based assessment of their discriminative potential. This structured approach enhances the interpretability and robustness of the extracted features, highlighting the importance of lip dynamics as an auxiliary modality in multimodal sign language recognition. The obtained results demonstrate that trajectory clustering can serve as a practical method for generating features, providing more accurate and context-sensitive gesture interpretation.

fingerspelling , lip articulation , multimodal recognition , trajectory clustering , unsupervised learning , visual speech

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Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana, 010008, Kazakhstan
Department of Computer Science, Lublin University of Technology, 36B Nadbystrzycka Str., Lublin, 20-618, Poland
Institute of Economics, Information Technologies and Professional Education, Zangir Khan West Kazakhstan Agrarion-Technical University, Uralsk, 090000, Kazakhstan

Faculty of Information Technologies
Department of Computer Science
Institute of Economics

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