Hidden Markov model parameters estimation with independent multiple observations and inequality constraints

Xia, Lisha; Fang, Huajing; Liu, Xiaoyong

doi:10.1049/iet-spr.2013.0505

IET signal process.

2014

DOI: 10.1049/iet-spr.2013.0505

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Hidden Markov model parameters estimation with independent multiple observations and inequality constraints

Lisha Xia

Huajing Fang

Xiaoyong Liu

Abstract: In this study, the authors focus on hidden Markov model (HMM) parameters estimation with independent multiple observations and non-linear inequality constraints. The parameters estimation process is divided into four steps: initialisation, parameters pre-estimation, parameters re-estimation and termination. The pre-estimation results are used to approximate nonlinear inequality constraints to linear inequality constraints. In parameters re-estimation step, the active-set optimisation is combined with the expec… Show more

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Cited by 2 publications

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“…Thereafter, for each individual, one HMM model is constructed by training a specified set of images for that individual. The training process depends on the estimation of the HMM parameters [8], such that the process is iterated several times before it converges. In the testing process, the algorithm computes the probability of the observations of unknown face image calibrated with the parameters derived from the training.…”

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confidence: 99%

Low-Complexity Face Recognition using a Multilevel DWT and TwoStates of Continuous HMM to recognize Noisy Images

Farhan¹,

Mahmuod²,

Al-Muifraje³

et al. 2017

IJET

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Abstract-Face recognition has become an important subject in modern life, especially in security and surveillance applications. This work introduces a face recognition method, which is characterized by high-speed, low-complexity, and high-efficiency in a noisy environment. The performance of this method is greatly improved by using a median filter, such that each image is filtered to eliminate the influence of noise and light illumination. Multiple levels of discrete wavelet transform are applied to the filtered image to reduce size and eliminate further noise. Subsequently, the resultant image is scanned using a window with a predefined overlap in raster fashion to construct a sequence of observation vectors used as the basis of a model. I. INTRODUCTION Face recognition has recently received considerable attention due to its important applications in many areas and fields such as credit card verification, security, and surveillance. The advantage of face recognition over other biometric techniques is the mechanism to recognize persons, which is frequently performed without requiring that the person have physical contact with the device, e.g. use of palm or fingers. The face image can be captured by a camera without informing the target person, especially in the case of criminal surveillance and apprehension.The methods introduced to recognize faces vary relative to the procedure used for feature extraction and type of classifier used. The artificial neural network [1], principle component analysis (PCA) [2], independent component analysis [3], linear discriminant analysis (LDA) [4], support vector machine [5] and K-nearest neighbour [6] are the most widely used methods. In addition, the hidden Markov model (HMM) [7] has been successfully used in face recognition during the last two decades. The advantage of applying HMM to face recognition is the flexibility of the selection of training models, such that it is easy to add or remove individuals. There is no need for re-training the overall system; however, a model with updated images must be re-trained.For dimensionality reduction, a particular feature extraction method is applied to form a sequence of observation vectors to model faces. Thereafter, for each individual, one HMM model is constructed by training a specified set of images for that individual. The training process depends on the estimation of the HMM parameters [8], such that the process is iterated several times before it converges. In the testing process, the algorithm computes the probability of the observations of unknown face image calibrated with the parameters derived from the training. The unknown face belongs to the person whose model computes the highest probability.The disadvantages of using more states of the HMM are: high computational complexities, large memory requirements, and low processing speed. The proposed work addresses the unfavourable features by using only two states of the HMM.

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Low-Complexity Face Recognition using a Multilevel DWT and TwoStates of Continuous HMM to recognize Noisy Images

Farhan¹,

Mahmuod²,

Al-Muifraje³

et al. 2017

IJET

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Data Mining and Analytics in the Process Industry: The Role of Machine Learning

et al. 2017

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Hidden Markov model parameters estimation with independent multiple observations and inequality constraints

Cited by 2 publications

References 31 publications

Low-Complexity Face Recognition using a Multilevel DWT and TwoStates of Continuous HMM to recognize Noisy Images

Low-Complexity Face Recognition using a Multilevel DWT and TwoStates of Continuous HMM to recognize Noisy Images

Data Mining and Analytics in the Process Industry: The Role of Machine Learning

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