ExtriDeNet: an intensive feature extrication deep network for hand gesture recognition

In this paper, a lightweighted Intensive Feature Extrication Deep Network (ExtriDeNet) is proposed for precise hand gesture recognition (HGR). ExtriDeNet primarily consists of two blocks: Intensive Feature Fusion Block (IFFB) and Intensive Feature Assimilation Block (IFAB). IFFB incorporates two different scaled filters $$3\times 3$$ and $$5 \times 5$$ to capture contextual features of hands, while IFAB is designed by embedding influential features of IFFB with two extreme minute and high-level feature responses from two receptive fields generated by employing $$1\times 1$$ and $$7\times 7$$ sized filters, respectively. The combination of multiscaled filters enriches the network with the most significant features and enhances the learnability of the network. Thus, the proposed ExtriDeNet efficiently defines the distinctive features of different hand gesture classes and achieves high performance as compared to state-of-the-art HGR approaches. The performance of the proposed network is evaluated on the standard datasets: MUGD, Finger Spelling, OUHands, NUS-I, NUS-II and HGR1 for both subject-dependent and subject-independent scheme.