Reduction of gesture feature dimension for improving the hand gesture recognition performance of numerical sign language

A major form of non-touch human-computer interaction (HCI) is hand gesture recognition. This is one of the appealing ways to interact with computers and a natural part of how we communicate. However, as a part of HCI, human hand gesture recognition is a challenging issue. From this point of view, this paper presents an effective hand gesture recognition system with hand feature selection for low cost video acquisition device. In this proposed model, hand features are extracted from video frame using discrete wavelet transformation and singular value decomposition. A genetic algorithm with effective fitness function is used to select optimal hand features by eliminating redundant and irrelevant features for improving the recognition performance. Finally, support vector machine is used to recognize the hand gestures for numerical hand gesture accuracy of American Sign Language. The proposed model is validated using a constructed hand gesture dataset. The proposed model is compared with non-feature selection based models, where the feature selection-embedded model outperforms the traditional hand recognition process.