FEW-SHOT LEARNING WITH PRE-TRAINED LAYERS INTEGRATION APPLIED TO HAND GESTURE RECOGNITION FOR DISABLED PEOPLE

Abstract

Employing vision-based hand gesture recognition for the interaction and communication of disabled individuals is highly beneficial. The hands and gestures of this category of people have a distinctive aspect, requiring the adaptation of a deep learning vision-based system with a dedicated dataset for each individual. To achieve this objective, the paper presents a novel approach for training gesture classification using few-shot samples. More specifically, the gesture classifiers are fine-tuned segments of a pre-trained deep network. The global framework consists of two modules. The first one is a base feature learner and a hand detector trained with normal people hand’s images; this module results in a hand detector ad hoc model. The second module is a learner sub-classifier; it is the leverage of the convolution layers of the hand detector feature extractor. It builds a shallow CNN trained with few-shot samples for gesture classification. The proposed approach enables the reuse of segments of a pre-trained feature extractor to build a new sub-classification model. The results obtained by varying the size of the training dataset have demonstrated the efficiency of our method compared to the ones of the literature.