Color and Illumination Independent Hand Tracking and Gesture Recognition

Recognition of human motion provides hints to understand human activities and gives opportunities to the development of a new human-computer interaction (HCI) interface. Hidden Markov Models (HMMs) are used for visual recognition of complex, structured hand gestures such as the ones found in a sign language, since they have proved their success in recognizing speech and handwriting. In this paper, we introduce a hand gesture recognition system to recognize gestures in real-time. Hand tracking is performed in two different ways. The first method is based on color segmentation and blob generation over the hand region, and the second method uses block matching and particle filtering algorithms to detect the moving hand which makes the system totally color and illumination independent. In both methods, extracted information is used as the input to the HMM based gesture recognizer.