Static hand gesture recognition using multi-dilated DenseNet-based deep learning architecture

ABSTRACT

Hand Gesture Recognition (HGR), a Human-Robot Interaction (HRI) create user interfaces to enhance safety and control. Several approaches are introduced to recognize static hand gestures effectively, however the classification efficiency suffered due to various lightings like natural light, artificial light and dark rooms increases the difficulty of accurate HGR. To tackle these problems, a Multi-Dilated Convolution-based DenseNet (MDCDN) architecture, a combination of multi-dilated convolution and DenseNet is proposed which extracts the features automatically. Finally, the benefits of high-level deep learning techniques are leveraged for the gesture recognition from hand. Python is used for architecture evaluation. The outcome of proposed is estimated in terms of accuracy, recall, F-measure, precision, etc, using ASL, ISL, Massey and HSR real datasets. Each dataset contains huge amount of gesture classes and their pictures have an equal amount of uniform and complex backgrounds. The proposed results are promising and provide better performance than existing methods.

KEYWORDS:

• Hand gesture recognition (HGR)
• pre-processing
• reduce noise
• multi-dilated convolution
• DenseNet

Data availability statement

Data sharing is not applicable to this article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Jogi John

Jogi John is a Ph.d. Pursuing Scholar, Computer Science expert who works in multiple domains, including Neural Network, Machine Learning, Deep Learning, Artificial Intelligence and Hand Gesture Recognition. His educational background is Computer Science and Engineering in the postgraduate program and Computer Science in the undergraduate program. His career activities' significant contribution is the Neural Network and Machine Learning.

Shrinivas Deshpande

Dr. Shrinivas Deshpande is a Professor of Computer Science and Engineering working in the P. G. Department of Computer Science and Technology, DCPE, HVPM, Amravati. He did his masters and PhD in the subject Computer Science and Engineering, having more than 30 years of teaching experience including 21 years at PG level. He is recognized PhD supervisor of Computer Science and Engineering at Sant Gadge Baba Amravati University, Amravati (MS). His area of research Data Bases, Data Science, AI & Machine Learning, Image Processing, and Software Engineering.