This research work emphasizes the utilization of machine learning and convolution neural network (CNN) to recognize hand gesture lively, in spite of variations in hand sizes and spatial position in the image by providing our own personalized system inputs as a dataset representing the gestures according to the classes developed and to implement our model that will identify and classify the gesture into one of the defined categories. CNN utilizes three layers, where two are hidden layers and another one is convolution. The proposed model has been designed with three classes containing personalized gestures. The classes considered here are first-aid, food, and water. This model can be used for in-flight comfort facilities by travelers and also where there is a need for the use of these gestures.
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