Hand posture dataset creation for gesture recognition

Abstract

This paper introduces a fast and feasible method for the collection of hand gesture samples. Currently, there are not solid reference databases and standards for the evaluation and comparison of developed algorithms in hand posture recognition, and more generally in gesture recognition. These are two important issues that should be solved in order to improve research results. Unlike previous hand image datasets, which creation usually involves many different people, sceneries and light conditions, we propose a simplified method that requires just a single person’ hand being recorded in a controlled light environment. Our method allows the generation of thousands of heterogeneous samples within hours, thus saving time and people’s efforts. The resulting dataset has been tested with a cascade classifier, although it may be used by most pattern recognition systems, and compared with a classical dataset obtaining similar results.