  The HGR is a quite challenging task as its performance is influenced by
various aspects such as illumination variations, cluttered backgrounds,
spontaneous capture, etc. The conventional CNN networks for HGR are following
two stage pipeline to deal with the various challenges: complex signs,
illumination variations, complex and cluttered backgrounds. The existing
approaches needs expert expertise as well as auxiliary computation at stage 1
to remove the complexities from the input images. Therefore, in this paper, we
proposes an novel end-to-end compact CNN framework: fine grained feature
attentive network for hand gesture recognition (Fit-Hand) to solve the
challenges as discussed above. The pipeline of the proposed architecture
consists of two main units: FineFeat module and dilated convolutional (Conv)
layer. The FineFeat module extracts fine grained feature maps by employing
attention mechanism over multiscale receptive fields. The attention mechanism
is introduced to capture effective features by enlarging the average behaviour
of multi-scale responses. Moreover, dilated convolution provides global
features of hand gestures through a larger receptive field. In addition,
integrated layer is also utilized to combine the features of FineFeat module
and dilated layer which enhances the discriminability of the network by
capturing complementary context information of hand postures. The effectiveness
of Fit- Hand is evaluated by using subject dependent (SD) and subject
independent (SI) validation setup over seven benchmark datasets: MUGD-I,
MUGD-II, MUGD-III, MUGD-IV, MUGD-V, Finger Spelling and OUHANDS, respectively.
Furthermore, to investigate the deep insights of the proposed Fit-Hand
framework, we performed ten ablation study.
