Unsupervised Clustering and Ensemble Learning for Classifying Lip Articulation in Fingerspelling.

Area of Science:

• Computer Vision
• Linguistics
• Human-Computer Interaction

Background:

• Sign language recognition faces challenges due to the variability and ambiguity of lip shapes.
• Existing methods often struggle with raw visual features, limiting accuracy.

Purpose of the Study:

• To develop a robust methodology for analyzing lip articulation in fingerspelling.
• To extract reliable visual patterns for enhanced sign language recognition.

Main Methods:

• Unsupervised clustering of lip movement trajectories to identify consistent articulatory patterns.
• Exploration of various cluster configurations and assessment of robustness.
• Model-based assessment of clustered representations for discriminative potential.

Main Results:

• Trajectory clustering effectively generates features for sign language recognition.
• The approach enhances interpretability and robustness of extracted lip features.
• Demonstrated the importance of lip dynamics as an auxiliary modality.

Conclusions:

• Unsupervised lip trajectory clustering is a practical method for feature generation.
• The methodology provides more accurate and context-sensitive gesture interpretation.
• Highlights the potential of lip dynamics in multimodal sign language recognition systems.