Gesture Recognition using Latent-Dynamic based Conditional Random Fields and Scalar Features

I N Yulita, M I Fanany, A M Arymurthy
2017 Journal of Physics, Conference Series  
The use of open and machine vision technologies for development of gesture recognition intelligent systems Kirill V Cherkasov, Irina V Gavrilova, Elena V Chernova et al. -Dynamic Gesture Recognition based on LeapMotion and HMM-CART Model Qixiang Zhang and Fang Deng -This content was downloaded from IP address 207.241.231.83 on 26/07 Abstract. The need for segmentation and labeling of sequence data appears in several fields. The use of the conditional models such as Conditional Random Fields is
more » ... l Random Fields is widely used to solve this problem. In the pattern recognition, Conditional Random Fields specify the possibilities of a sequence label. This method constructs its full label sequence to be a probabilistic graphical model based on its observation. However, Conditional Random Fields can not capture the internal structure so that Latent-based Dynamic Conditional Random Fields is developed without leaving external dynamics of inter-label. This study proposes the use of Latent-Dynamic Conditional Random Fields for Gesture Recognition and comparison between both methods. Besides, this study also proposes the use of a scalar features to gesture recognition. The results show that performance of Latent-dynamic based Conditional Random Fields is not better than the Conditional Random Fields, and scalar features are effective for both methods are in gesture recognition. Therefore, it recommends implementing Conditional Random Fields and scalar features in gesture recognition for better performance
doi:10.1088/1742-6596/812/1/012113 fatcat:ttym4c7nizfw7jbd5tbmeb5ffu