Dynamic gesture recognition has been studied actually for it big application in several areas such as virtual reality, games and sign language. But some problems have to be solved in computer applications, such as response time and classification rate, which directly affect the real-time usage. This paper proposes a novel algorithm called Convex Invariant Position Based on Ransac which improved the good results in dynamic gesture recognition problem. The proposed method is combined with a adapted PSO variation to reduce features and a HMM and three DTW variations as classifiers.

Dynamic gesture recognition systems based on computer vision techniques have been frequently used in some fields such as medical, games and sign language. Usually, these systems have a time execution problem caused by the elevated number of features or attributes extracted for gesture classification. This work presents a system for dynamic gesture recognition that uses Particle Swarm Optimization to reduce the feature vector while increases the classification capability. The system FSSGR, Feature Selection System to Dynamic Gesture Recognition, solved the gesture recognition problem in RPPDI dataset, achieving 99.21% of classification rate with the same vectors size of previous works on the same database, although with a better response time.