A multi-modal framework for continuous and isolated hand gesture recognition utilizing movement epenthesis detection

Gesture recognition, having multitudinous applications in the real world, is one of the core areas of research in the field of human-computer interaction. In this paper, we propose a novel method for isolated and continuous hand gesture recognition utilizing the movement epenthesis detection and removal. For this purpose, the present work detects and removes the movement epenthesis frames from the isolated and continuous hand gesture videos. In this paper, we have also proposed a novel modality based on the temporal difference that extracts hand regions, removes gesture irrelevant factors and provides temporal information contained in the hand gesture videos. Using the proposed modality and other modalities such as the RGB modality, depth modality and segmented hand modality, features are extracted using Googlenet Caffe Model. Next, we derive a set of discriminative features by fusing the acquired features that form a feature vector representing the sign gesture in question. We have designed and used a Bidirectional Long Short-Term Memory Network (Bi-LSTM) for classification purpose. To test the efficacy of our proposed work, we applied our method on various publicly available continuous and isolated hand gesture datasets like ChaLearn LAP IsoGD, ChaLearn LAP ConGD, IPN Hand, and NVGesture. We observe in our experiments that our proposed method performs exceptionally well with several individual modalities as well as combination of modalities of these datasets. The combined effect of the proposed modality and movement epenthesis frames removal led to significant improvement in gesture recognition accuracy and considerable reduction in computational burden. Thus the obtained results advocate our proposed approach to be at par with the existing state-of-the-art methods.