Vision Based Multi-Feature Hand Gesture Recognition For Indian Sign Language Manual Signs
Publicado en línea: 01 mar 2016
Páginas: 124 - 147
Recibido: 30 ene 2016
Aceptado: 04 ene 2016
DOI: https://doi.org/10.21307/ijssis-2017-863
Palabras clave
© 2016 Gajanan K. Kharate et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Indian sign language (ISL) is the main communication medium among deaf Indians. An ISL vocabulary show that the hand plays a significant role in ISL. ISL includes static and dynamic hand gesture recognition. The main aim of this paper is to present multi-feature static hand gesture recognition for alphabets and numbers. Here, comparative analysis of various feature descriptors such as chain code, shape matrix, Fourier descriptor, 7 Hu moments, and boundary moments is done. Multi-feature fusion descriptor is designed using contour (Boundary moments, Fourier descriptor) and region based (7Hu moments) descriptors. Analysis of this new multi-feature descriptor is done in comparison with other individual descriptors and it showed noteworthy results over other descriptors. Three classification methods such as, Nearest Mean Classifier (NMC), k-Nearest Neighborhood (k-NN) and Naive Bayes classifier are used for classification and comparison. New Multi-feature fusion descriptor shows high recognition rate of 99.61% among all with k-NN. Real time recognition for number signs 0-9, of fusion descriptor with NMC gave 100% accuracy