Unified Learning Approach for Egocentric Hand Gesture Recognition and Fingertip Detection
This work addresses the problem of efficient and accurate egocentric hand gesture recognition and fingertip detection for human-computer interaction in head-mounted devices.
This paper introduces a unified learning approach for egocentric hand gesture recognition and fingertip detection using a single convolutional neural network. The method predicts finger class probabilities and fingertip positions in one forward propagation, outperforming existing fingertip detection approaches like Direct Regression and Heatmap-based frameworks.
Head-mounted device-based human-computer interaction often requires egocentric recognition of hand gestures and fingertips detection. In this paper, a unified approach of egocentric hand gesture recognition and fingertip detection is introduced. The proposed algorithm uses a single convolutional neural network to predict the probabilities of finger class and positions of fingertips in one forward propagation. 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. Experimental results show that the proposed method outperforms the existing fingertip detection approaches including the Direct Regression and the Heatmap-based framework. The effectiveness of the proposed method is also shown in-the-wild scenario as well as in a use-case of virtual reality.