An ant learning algorithm for gesture recognition with one-instance training

In this paper, we introduce a novel gesture recognition algorithm named the ant learning algorithm (ALA), which aims at eliminating some of the limitations with the current leading algorithms, especially Hidden Markov Models. It requires minimal training instances and greatly reduces the computational overhead required by both training and classification. ALA takes advantage of the pheromone mechanism from ant colony optimization. It uses pheromone tables to represent gestures, which scales well with gesture complexity. Our experimental results show that ALA can achieve a high recognition accuracy of 91.3% with only one training instance, and exhibits good generalization.