Face recognition based on separable lattice 2-D HMM with state duration modeling

Takahashi, Yoshiaki; Tamamori, Akira; Nankaku, Yoshihiko; Tokuda, Keiichi

doi:10.1109/icassp.2010.5495625

Cited by 7 publications

(6 citation statements)

References 5 publications

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“…where P ( ) is a prior distribution and P (o) is evidence. Model parameters are estimated as posterior distribution P ( | o), and posterior distribution is integrated out in (20) so that the effect of overfitting is mitigated. That is, the Bayesian criterion has higher generalization ability than the ML criterion when there is insufficient training data.…”

Section: Bayesian Criterionmentioning

confidence: 99%

“…is calculated using (20). Although predictive distribution includes a complicated expectation calculation, the z (1) t (1) z (2) t (2) a (2) π (2) a (1) π (1) ϕ (1) α (1) ϕ (2) α (2) Dotted rectangles represent hyperparameters same approximation based on the VB method as that in posterior distribution training can be applied, and the lower bound of predictive distribution F (test) is defined as (Q(x , z , Λ)) (39) The same conditional independence assumption of the posterior distribution is used as in (26).…”

Section: Predictive Distribution Predictive Distributionmentioning

confidence: 99%

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A bayesian framework for image recognition based on hidden markov eigen‐image models

Sawada

Hashimoto

Nankaku

et al. 2018

IEEJ Transactions Elec Engng

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An image recognition method based on hidden Markov eigen‐image models (HMEMs) using a Bayesian framework is proposed and experimentally evaluated. HMEMs have been proposed as a model with two advantageous properties: invariances to the size and location of the object to be recognized, and a linear feature extraction based on statistical analysis. However, the training of HMEMs easily falls into the overfitting problem because HMEMs have a complex model structure, especially when there is insufficient training data. This study aims to accurately estimate HMEMs using the variational Bayesian (VB) method. The VB method can utilize prior distributions representing useful prior information and is expected to have a high generalization ability due to the marginalization of model parameters. Furthermore, to relax the local maximum problem in the VB method, the deterministic annealing expectation maximization algorithm is applied to train HMEMs. Experiments on face recognition indicate that the proposed method offers a significantly improved image recognition performance. Additionally, comparative experimental results show that the proposed method is more robust to geometric variations than convolutional neural networks when the amount of training data is insufficient. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: Bayesian Criterionmentioning

confidence: 99%

Section: Predictive Distribution Predictive Distributionmentioning

confidence: 99%

A bayesian framework for image recognition based on hidden markov eigen‐image models

Sawada

Hashimoto

Nankaku

et al. 2018

IEEJ Transactions Elec Engng

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“…To cope with this problem, the training algorithm for SL2D-HMMs using the variational EM algorithm were derived in [8], where the loglikelihood can be approximated by the variational lower bound. Although some extensions of SL2D-HMMs have been proposed, e.g., a structure for rotational variations [10], explicit state duration modeling [11], and a structure with multiple horizontal/vertical Markov chains [12], this paper uses an original form of SL2D-HMMs.…”

Section: Separable Lattice 2-d Hmmsmentioning

confidence: 99%

Image Recognition Based on Separable Lattice Trajectory 2-D HMMs

Tamamori

Nankaku

Tokuda

2014

IEICE Trans. Inf. & Syst.

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Akira TAMAMORI†a) , Nonmember, Yoshihiko NANKAKU †b) , and Keiichi TOKUDA †c) , Members SUMMARYIn this paper, a novel statistical model based on 2-D HMMs for image recognition is proposed. Recently, separable lattice 2-D HMMs (SL2D-HMMs) were proposed to model invariance to size and location deformation. However, their modeling accuracy is still insufficient because of the following two assumptions, which are inherited from 1-D HMMs: i) the stationary statistics within each state and ii) the conditional independent assumption of state output probabilities. To overcome these shortcomings in 1-D HMMs, trajectory HMMs were proposed and successfully applied to speech recognition and speech synthesis. This paper derives 2-D trajectory HMMs by reformulating the likelihood of SL2D-HMMs through the imposition of explicit relationships between static and dynamic features. The proposed model can efficiently capture dependencies between adjacent observations without increasing the number of model parameters. The effectiveness of the proposed model was evaluated in face recognition experiments on the XM2VTS database.

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“…HSMM has overcome the strict geometric distribution restriction of HMM when describing state duration and the duration of any distribution can be depicted. In recent years, HSMM is extensively applied to voice recognition [9] and failure prediction [10]. Therefore, HSMM is suitable for modeling and prediction of HSC system.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Anomaly in the Behavior Characteristics of Operators in HSMM-based Multi-UAV Supervisory Control System

Zhang¹,

Yuan²,

Ma³

2015

Proceedings of the 2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics

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： Solo operation of multi-unmanned Aerial Vehicle (UAV) will be the trend of future, and operators play a significant role in the UAV supervisory control system, but human may be impacted by all kinds of factors, which may decline the performance of UAV system. In this paper, modeling is conducted for the behavioral characteristics of operators in the multi-UAV supervisory control system with improved Hidden semi-Markov Model (HSMM), and the relationship between the predication result and anomaly of HSMM model is analyzed. This method can be applied for monitoring the anomaly in the behaviors of operators, which is of strong and practical significance in the multi-UAV supervisor control system and Human Supervisory Control (HSC) system.

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Face recognition based on separable lattice 2-D HMM with state duration modeling

Cited by 7 publications

References 5 publications

A bayesian framework for image recognition based on hidden markov eigen‐image models

A bayesian framework for image recognition based on hidden markov eigen‐image models

Image Recognition Based on Separable Lattice Trajectory 2-D HMMs

Analysis of Anomaly in the Behavior Characteristics of Operators in HSMM-based Multi-UAV Supervisory Control System

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