Saliency-guided improvement for hand posture detection and recognition

Abstract

To detect and recognise hand postures against complex backgrounds, we propose a novel model that is constructed by the integration of image saliency and skin information. Although a skin model is a simple and efficient strategy by which to locate skin regions within images, it is easily affected by complex backgrounds, e.g. skin-like background regions and various lighting conditions. To solve this problem, we propose a general image saliency detection method that is then integrated with skin information to improve the performance of hand posture detection. Lastly, a linear Support Vector Machine (SVM) is adopted to recognise hand postures according to the results of hand posture detection. In the experiment, we tested the performance of the proposed image saliency detection method over seven state-of-the-art methods. The saliency-based hand posture detection and recognition model was also evaluated. These experiments show that the proposed model has stable performance for a wide range of images.