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dc.contributor.authorKhari, Manju
dc.contributor.authorGarg, Aditya Kumar
dc.contributor.authorGonzález-Crespo, Rubén (1)
dc.contributor.authorVerdú, Elena (1)
dc.description.abstractIn 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.es_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligencees_ES
dc.relation.ispartofseries;vol. 5, nº 7
dc.subjectconvolution neural networkes_ES
dc.subjectgesture recognitiones_ES
dc.subjectimage processinges_ES
dc.subjectsign languagees_ES
dc.titleGesture Recognition of RGB and RGB-D Static Images Using Convolutional Neural Networkses_ES
dc.typeArticulo Revista Indexadaes_ES

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