Invariant lighting hand posture classification

Hand posture classification is a key problem for many human computer interaction applications. However, this is not a simple problem. In this paper, we propose to decompose the hand posture classification problem into 2 steps. In the first step, we detect skin regions using a very fast algorithm of color segmentation based on thresholding technique. This segmentation is robust to lighting condition thank to a step of color normalization using neural network. In the second step, each skin region will be classified into one of hand posture class using Cascaded Adaboost technique. The contributions of this paper are: (i) By applying a step of color normalization, the posture classification rate is significantly improved under varying lighting condition; (ii) The cascaded Adaboost technique has been studied for the problem of face detection (2 classes). In this paper, it will be studied and evaluated in more detail in a problem of classification of hand postures (multi-classes).