Compact Deeplearning Convolutional Neural Network based Hand Gesture Classifier Application for Smart Mobile Edge Computing

Modern Deeplearning Convolutional Neural Network-based image classifier are now becoming extremely high performing with acute accuracy. Although this provides a reliable classification system the sheer computational power for this type of classification makes it very difficult to run in remote environments lower computational power. In smart next- generation mobile edge computing platforms, the applications need to be lightweight and very robust and faster to provide classification results. In the context of the hand-operated smart factory, the classification of a given hand gesture is vital for its accurate operation. In this research, we have designed a hand gesture image collection and label model and later train a deeplearning convolutional neural network to classify this model with the highest 98% accuracy. Later we introduced the node pruning system improvement which compresses the model while retraining the same accuracy rate of 98%. This type of low weight pruned deeplearning convolutional neural network will help build a lightweight Mobile edge-based server with high accuracy and lower computation overheard based application that is suitable for smart factory based remote applications.