RIEVL: recursive induction learning in hand gesture recognition

Abstract

Presents a recursive inductive learning scheme that is able to acquire hand pose models in the form of disjunctive normal form expressions involving multivalued features. Based on an extended variable-valued logic, our rule-based induction system is able to abstract compact rule sets from any set of feature vectors describing a set of classifications. The rule bases which satisfy the completeness and consistency conditions are induced and refined through five heuristic strategies. A recursive induction learning scheme in the RIEVL algorithm is designed to escape local minima in the solution space. A performance comparison of RIEVL with other inductive algorithms, ID3, NewID, C4.5, CN2, and HCV, is given in the paper. In the experiments with hand gestures, the system produced the disjunctive normal form descriptions of each pose and identified the different hand poses based on the classification rules obtained by the RIEVL algorithm. RIEVL classified 94.4 percent of the gesture images in our testing set correctly, outperforming all other inductive algorithms.