Unified Learning Approach for Egocentric Hand Gesture Recognition and Fingertip Detection

TL;DR

This paper presents a unified, fast convolutional neural network approach for egocentric hand gesture recognition and fingertip detection, outperforming existing methods in accuracy and efficiency, suitable for real-world applications like virtual reality.

Contribution

A novel single-network framework that simultaneously recognizes hand gestures and detects fingertips efficiently, improving speed and accuracy over prior separate or multi-stage methods.

Findings

Outperforms existing fingertip detection methods

Operates efficiently in real-time scenarios

Effective in-the-wild and virtual reality applications

Abstract

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.