  We propose a novel appearance-based gesture recognition algorithm using
compressed domain signal processing techniques. Gesture features are extracted
directly from the compressed measurements, which are the block averages and the
coded linear combinations of the image sensor's pixel values. We also improve
both the computational efficiency and the memory requirement of the previous
DTW-based K-NN gesture classifiers. Both simulation testing and hardware
implementation strongly support the proposed algorithm.
