Dynamic footprint-based person recognition method using a hidden markov model and a neural network

2004 ◽  
Vol 19 (11) ◽  
pp. 1127-1141 ◽  
Author(s):  
Jin-Woo Jung ◽  
Tomomasa Sato ◽  
Zeungnam Bien
Keyword(s):  
Neural Network ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Recognition Method ◽  
Person Recognition ◽  
Dynamic Footprint

scholarly journals A Hybrid Hidden Markov Model for Pipeline Leakage Detection

2021 ◽  
Vol 11 (7) ◽  
pp. 3138
Author(s):  
Mingchi Zhang ◽  
Xuemin Chen ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Deep Neural Network ◽  
Hidden Markov ◽  
Gaussian Mixture ◽  
State Sequence ◽  
Non Linear

In this paper, a deep neural network hidden Markov model (DNN-HMM) is proposed to detect pipeline leakage location. A long pipeline is divided into several sections and the leakage occurs in different section that is defined as different state of hidden Markov model (HMM). The hybrid HMM, i.e., DNN-HMM, consists of a deep neural network (DNN) with multiple layers to exploit the non-linear data. The DNN is initialized by using a deep belief network (DBN). The DBN is a pre-trained model built by stacking top-down restricted Boltzmann machines (RBM) that compute the emission probabilities for the HMM instead of Gaussian mixture model (GMM). Two comparative studies based on different numbers of states using Gaussian mixture model-hidden Markov model (GMM-HMM) and DNN-HMM are performed. The accuracy of the testing performance between detected state sequence and actual state sequence is measured by micro F1 score. The micro F1 score approaches 0.94 for GMM-HMM method and it is close to 0.95 for DNN-HMM method when the pipeline is divided into three sections. In the experiment that divides the pipeline as five sections, the micro F1 score for GMM-HMM is 0.69, while it approaches 0.96 with DNN-HMM method. The results demonstrate that the DNN-HMM can learn a better model of non-linear data and achieve better performance compared to GMM-HMM method.

scholarly journals A Fusion Recognition Method Based on Multifeature Hidden Markov Model for Dynamic Hand Gesture

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Guoliang Chen ◽  
Kaikai Ge
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Calculation Model ◽  
Opening Angle ◽  
Hand Gesture ◽  
Leap Motion ◽  
Recognition Method ◽  
Hand Gestures ◽  
Multiple Features

In this paper, a fusion method based on multiple features and hidden Markov model (HMM) is proposed for recognizing dynamic hand gestures corresponding to an operator’s instructions in robot teleoperation. In the first place, a valid dynamic hand gesture from continuously obtained data according to the velocity of the moving hand needs to be separated. Secondly, a feature set is introduced for dynamic hand gesture expression, which includes four sorts of features: palm posture, bending angle, the opening angle of the fingers, and gesture trajectory. Finally, HMM classifiers based on these features are built, and a weighted calculation model fusing the probabilities of four sorts of features is presented. The proposed method is evaluated by recognizing dynamic hand gestures acquired by leap motion (LM), and it reaches recognition rates of about 90.63% for LM-Gesture3D dataset created by the paper and 93.3% for Letter-gesture dataset, respectively.

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