Real-time hand posture analysis based on neural network

In this paper, a modified Neural Gas algorithm is proposed and used to approximate hand topology. As original Neural Gas algorithm is intractable for real-time applications, some optimization such as unnecessary adaption removal and simple learning rate function are introduced to make it applicable for real-time applications. With segmented hand area, the topology representation can be obtained based on neural network. The topology based representation of hand shape will further facilitate both fingertip localization and posture recognition. Experiments show the accuracy and the speed of our method can satisfy realtime requirements of interaction applications, even on mobile devices.