Real Time Hand Gesture Recognition Using Random Forest and Linear Discriminant Analysis

This paper presents a real-time hand gesture detection and recognition method. Proposed method consists of three steps - detection, validation and recognition. In the detection stage, several areas, estimated to contain hand shapes are detected by random forest hand detector over the whole image. The next steps are validation and recognition stages. In order to check whether each area contains hand or not, we used Linear Discriminant Analysis. The proposed work is based on the assumption that samples with similar posture are distributed near each other in high dimensional space. So, training data used for random forest are also analyzed in three dimensional space. In the reduced dimensional space, we can determine decision conditions for validation and classification. After detecting exact area of hand, we need to search for hand just in the nearby area. It reduces processing time for hand detection process.