A pose-based hand image classification method retrainable on embedded target devices

State of the Art solutions for image classification are nowadays mostly based on Convolutional Neural Networks (CNNs), With respect to running them for inference, training CNNs is a much more resource demanding job. For this reason, embedded devices designed to execute neural networks are normally not suitable to train the same networks. Hand image classification is becoming increasingly popular in several application areas, by empowering embedded systems with the ability to recognise hand gestures or grasp types. In this field, making it possible to retrain the hand image classifier directly on the embedded device would allow an easy customization of classification task according to application specific needs. In this paper we present a method based on transfer learning which solves this problem by first extracting hand keypoints, using with a dedicated neural network, and then applying on the resulting data a lightweight classifier, easily retrainable directly on the target embedded system. The proposal has been validated on a new freely distributed dataset, outperforming state of the art solutions in the specific task.