ABSTRACT
This chapter focuses on recent research in Randomized Decision
Tree (RDT) algorithm; in particular, how to classify human face
and body using pixelwise classification of depth image. Similar to
popular machine learning algorithms, training of RDT is also com-
putation intensive. This chapter shows a more efficient technique
to reduce the training time of the RDT algorithm. Hence, it is
suitable for power-constrained devices. Besides, two applications
are presented in this chapter to show the efficiency of the technique:
(i) a fall detection system that monitor human fall down; (ii) a
human-computer interface system that enable human to use nose
and mouth to control computer mouse. The applications end with
experimental results and performance evaluations.
