Minimizing redundancy in hand dynamic features for enhanced sign language recognition

Abstract

This paper presents a wavelet-based framework for 3-D sign language recognition that outperforms conventional ZCA approaches by preserving critical spatiotemporal relationships in hand kinematics. We formulate hand dynamics as a wavelet exponent energy problem using Daubechies transforms, where joint coordinates, mother coefficients, and window sizes are jointly optimized, avoiding PCA’s disruptive axis rotation while maintaining interpretable feature orientations. The proposed method combines this wavelet energy representation with an ensemble deep learning feature selection layer using binary masking ( ${\lambda }_1=0.10$ , ${\lambda }_2=0.01$ ) and regularization to eliminate redundancy, achieving 94.09% accuracy (vs. 92.75% for PCC+ZCA (SVD)) with 4.3 $\times$ faster inference on Turkish SL datasets. Statistical validation (ANOVA, $p<0.001$ ) confirms significant improvements over ZCA-derived features ( $t=24.15$ ), with weight analysis revealing proximal arm kinematics as dominant recognition factors. The framework’s computational efficiency (0.12ms inference) and preservation of kinematic relationships demonstrate its superiority for real-life SLR applications compared to both PCC and ZCA approaches.