  In human-computer interaction or sign language interpretation, recognizing
hand gestures and detecting fingertips become ubiquitous in computer vision
research. In this paper, a unified approach of convolutional neural network for
both hand gesture recognition and fingertip detection is introduced. The
proposed algorithm uses a single network to predict the probabilities of finger
class and positions of fingertips in one forward propagation of the network.
Instead of directly regressing the positions of fingertips from the fully
connected layer, the ensemble of the position of fingertips is regressed from
the fully convolutional network. Subsequently, the ensemble average is taken to
regress the final position of fingertips. Since the whole pipeline uses a
single network, it is significantly fast in computation. The proposed method
results in remarkably less pixel error as compared to that in the direct
regression approach and it outperforms the existing fingertip detection
approaches including the Heatmap-based framework.
