DCA-based unimodal feature-level fusion of orthogonal moments for Indian sign language dataset

Sign language recognition system classifies signs made by hand gestures. An adequate number of features are required to represent the shape variations of sign language. As compared to individual feature set, a combination of features can be effective due to the fact that a particular feature set represents different shape information. A simple concatenation results in large feature vector size and increases the classification computational complexity. Discriminant correlation analysis (DCA)-based unimodal feature-level fusion has been applied on uniform as well as complex background Indian sign language datasets. DCA is a feature-level fusion technique that takes into account the class associations while combining the feature sets. It maximises the inter-class separability of two feature sets and also minimises the intra-class separability while performing the feature fusion. The objective of DCA-based unimodal feature fusion technique is to combine different feature sets into a single feature vector with more discriminative power. The performance of proposed framework is compared with individual orthogonal moment-based feature sets and canonical correlation analysis (CCA)-based feature fusion technique. Results show that in comparison to individual features and CCA-based fused features, DCA is an effective technique in terms of improved accuracy, reduced feature vector size and smaller classification time.