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In this paper, we introduce a new benchmark dataset named IPN Hand withsufficient size, variety, and real-world elements able to train and evaluatedeep neural networks. This dataset contains more than 4,000 gesture samples and800,000 RGB frames from 50 distinct subjects. We design 13 different static anddynamic gestures focused on interaction with touchless screens. We especiallyconsider the scenario when continuous gestures are performed without transitionstates, and when subjects perform natural movements with their hands asnon-gesture actions. Gestures were collected from about 30 diverse scenes, withreal-world variation in background and illumination. With our dataset, theperformance of three 3D-CNN models is evaluated on the tasks of isolated andcontinuous real-time HGR. Furthermore, we analyze the possibility of increasingthe recognition accuracy by adding multiple modalities derived from RGB frames,i.e., optical flow and semantic segmentation, while keeping the real-timeperformance of the 3D-CNN model. Our empirical study also provides a comparisonwith the publicly available nvGesture (NVIDIA) dataset. The experimentalresults show that the state-of-the-art ResNext-101 model decreases about 30%accuracy when using our real-world dataset, demonstrating that the IPN Handdataset can be used as a benchmark, and may help the community to step forwardin the continuous HGR. Our dataset and pre-trained models used in theevaluation are publicly available at https://github.com/GibranBenitez/IPN-hand.

IPN Hand: A Video Dataset and Benchmark for Real-Time Continuous Hand Gesture Recognition