Open AccessDBN-Based Fingerspelling Recognition Approach using Feature fusion
Author(s) -
Yuan Hu
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1302/2/022023
Subject(s) - computer science , feature (linguistics) , artificial intelligence , gesture , sign language , gesture recognition , fusion , pattern recognition (psychology) , speech recognition , philosophy , linguistics
Sign language recognition offers effective and precise approach of recognizing gestures or postures. In this work, a vision-based framework is presented for recognizing fingerspelling alphabets and a comparison is conducted to show the efficiency of feature fusion. Fused features and Deep Belief Network are used in the proposed framework. In the experiments stage, a comparison between the fused features and the individual ones is performed by using two public fingerspelling datasets. Experiment results show the improvement of the feature fusion.