Hand posture recognition using real-time artificial evolution

Abstract : In this paper, we present a hand posture recognition system (configuration and position) we designed as part of a gestural man-machine interface. After a simple image preprocessing, the parameter space (corresponding to the configuration and spatial position of the user's hand) is directly explored using a population of points evolved via an Evolution Strategy. Giving the priority to exploring the parameter space rather than the image, is an alternative to the classical generalisation of the Hough Transform and allows to meet the real-time constraints of the project. The application is an Augmented Reality prototype for a long term exhibition at the Cité des Sciences, Paris. As it will be open to the general public, rather than using conventional peripherals like a mouse or a joystick, a more natural interface has been chosen, using a microcamera embedded into virtual reality goggles in order to exploit the images of the user's hand as input data and enable the user to manipulate virtual objects without any specific training.
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Benoît Kaufmann, Jean Louchet, Evelyne Lutton. Hand posture recognition using real-time artificial evolution. EVOIASP, Evolutionary Computation in Image Analysis and Signal Processing, EvoApplications 2010, Apr 2010, Istanbul, Turkey. pp.251-260. ⟨hal-00783828⟩

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