TY - JOUR
KW - Image Processing
KW - Gesture Recognition
KW - Sign Language
KW - Convolutional Neural Network (CNN)
AU - Rubén González-Crespo
AU - Elena Verdú
AU - Manju Khari
AU - Aditya Kumar Garg
AB - In this era, the interaction between Human and Computers has always been a fascinating field. With the rapid development in the field of Computer Vision, gesture based recognition systems have always been an interesting and diverse topic. Though recognizing human gestures in the form of sign language is a very complex and challenging task. Recently various traditional methods were used for performing sign language recognition but achieving high accuracy is still a challenging task. This paper proposes a RGB and RGB-D static gesture recognition method by using a fine-tuned VGG19 model. The fine-tuned VGG19 model uses a feature concatenate layer of RGB and RGB-D images for increasing the accuracy of the neural network. Finally, on an American Sign Language (ASL) Recognition dataset, the authors implemented the proposed model. The authors achieved 94.8% recognition rate and compared the model with other CNN and traditional algorithms on the same dataset.
IS - Regular Issue
M1 - 7
N2 - In this era, the interaction between Human and Computers has always been a fascinating field. With the rapid development in the field of Computer Vision, gesture based recognition systems have always been an interesting and diverse topic. Though recognizing human gestures in the form of sign language is a very complex and challenging task. Recently various traditional methods were used for performing sign language recognition but achieving high accuracy is still a challenging task. This paper proposes a RGB and RGB-D static gesture recognition method by using a fine-tuned VGG19 model. The fine-tuned VGG19 model uses a feature concatenate layer of RGB and RGB-D images for increasing the accuracy of the neural network. Finally, on an American Sign Language (ASL) Recognition dataset, the authors implemented the proposed model. The authors achieved 94.8% recognition rate and compared the model with other CNN and traditional algorithms on the same dataset.
PY - 2019
SP - 22
EP - 27
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Gesture Recognition of RGB and RGB-D Static Images Using Convolutional Neural Networks
UR - https://www.ijimai.org/journal/sites/default/files/files/2019/09/ijimai20195_7_2_pdf_18405.pdf
VL - 5
SN - 1989-1660
ER -