Hand Tracking and Gesture Recognition Using Echo State Neural Networks

Peter Fillo

Abstract. Tracking an object in a video sequence is a complex problem which presents one of the fundamental task of image processing. One of the many use cases is controlling using hand gestures in Human-Computer Interaction. This paper introduces real-time hand recognition and tracking in video sequence with a classification of performed hand gestures. Hand recognition is based on foreground segmentation and skin region detection. Attributes of hand movements are being recorded and used as an input to a echo state neural network which performs hand gesture classification. Work presents proposed tracking algorithm and first results of gesture recognition.

Echo state neural networks

The echo state neural networks (ESN) can solve many problems based on time context. ESN networks are the special type of recurrent neural networks (RNN), but the randomly initialized hidden layer contains a high number of neurons, called dynamic reservoir (DR).

Training ESN

The training of ESN networks consist of following steps, outlined by [6]:

  1. Initialization an untrained ESN which has the echo state property.
  2. Sample network training dynamics
  3. Compute output weights

Hand tracking algorithm and implementation

Gesture recognition and experiments