Molina J.,Video Processing and Understanding Laboratory Laboratorio C 111 |
Pajuelo J.A.,Video Processing and Understanding Laboratory Laboratorio C 111 |
Escudero-Vinolo M.,Video Processing and Understanding Laboratory Laboratorio C 111 |
Bescos J.,Video Processing and Understanding Laboratory Laboratorio C 111 |
Martinez J.M.,Video Processing and Understanding Laboratory Laboratorio C 111
Machine Vision and Applications | Year: 2014
The use of hand gestures offers an alternative to the commonly used human-computer interfaces (i.e. keyboard, mouse, gamepad, voice, etc.), providing a more intuitive way of navigating among menus and in multimedia applications. This paper presents a dataset for the evaluation of hand gesture recognition approaches in human-computer interaction scenarios. It includes natural data and synthetic data from several State of the Art dictionaries. The dataset considers single-pose and multiple-pose gestures, as well as gestures defined by pose and motion or just by motion. Data types include static pose videos and gesture execution videos - performed by a set of eleven users and recorded with a time-of-flight camera - and synthetically generated gesture images. A novel collection of critical factors involved in the creation of a hand gestures dataset is proposed: capture technology, temporal coherence, nature of gestures, representativeness, pose issues and scalability. Special attention is given to the scalability factor, proposing a simple method for the synthetic generation of depth images of gestures, making possible the extension of a dataset with new dictionaries and gestures without the need of recruiting new users, as well as providing more flexibility in the point-of-view selection. The method is validated for the presented dataset. Finally, a separability study of the pose-based gestures of a dictionary is performed. The resulting corpus, which exceeds in terms of representativity and scalability the datasets existing in the State Of Art, provides a significant evaluation scenario for different kinds of hand gesture recognition solutions. © 2013 Springer-Verlag Berlin Heidelberg.