Face verification using D-HMM and adaptive K-means clustering

Vaseghi, Behrouz; Hashemi, Seyedeh Somayeh

doi:10.1109/icbnmt.2011.6155939

Cited by 5 publications

(6 citation statements)

References 19 publications

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“…Thereafter, the trend is to reduce the size of the observation vectors by using dimensionality reduction methods instead of pixel intensities. In [9,10,11,12,13,14], Discrete Cosine Transform (DCT) was used to extract features from images. The Wavelet Transform (WT) is also used either in continuous or discrete form as in [15,16,17,18,19,20,21,22,23,24].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Face Recognition using Maximum Variance and SVD of Order Statistics with only Three States of Hidden Markov Model

Farhan¹,

Al-Muifraje²,

Saeed³

2016

IJCA

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This paper presents a fast face recognition (FR) method using only three states of Hidden Markov Model (HMM), where the number of states is a major effective factor in computational complexity. Most of the researchers believe that each state represents one facial region, so they used five states or more according to the number of facial regions. In this work, a different idea has been proven, where the number of states is independent of the number of facial regions. The image is resized to 56x56, and order-statistic filters are used to improve the preprocessing operations and thereby reducing the influence of the illumination and noise. Up to three coefficients of Singular Value Decomposition (SVD) are utilized to describe overlapped blocks of size 5x56. Experimental results show that the proposed work manages to achieve 100% recognition rate on ORL face database using the maximum variance and two coefficients of SVD and can, therefore, be considered as the fastest face recognition type.

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Section: Related Workmentioning

confidence: 99%

“…Most of the researchers used 5-state HMM [8,9,10,11,12,13,16,18,20,21,25,26,27,30] and others used 7-state HMM [22,23,28]. In [19], 64 states were used, while in [24], the authors used 9-state HMM for Yale database and 15state HMM for ORL database.…”

Section: Related Workmentioning

confidence: 99%

Face Recognition using Maximum Variance and SVD of Order Statistics with only Three States of Hidden Markov Model

Farhan¹,

Al-Muifraje²,

Saeed³

2016

IJCA

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“…[18][19][20] HMM consists of two interrelated processes: (1) an underlying, unobservable Markov chain with a finite number of states and (2) a set of random probability density functions which determine the distribution of observation value in each state. [18][19][20] HMM consists of two interrelated processes: (1) an underlying, unobservable Markov chain with a finite number of states and (2) a set of random probability density functions which determine the distribution of observation value in each state.…”

Section: Hidden Markov Modelmentioning

confidence: 99%

Hidden Markov Models for background clutter

Yang

Zhang

et al. 2013

Opt. Eng

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The development of a target acquisition performance model for an electro-optical imaging system is seriously affected by the description of the target and background characteristics at present. Based on the Hidden Markov Model (HMM), a different clutter metric is proposed to quantify the influence of background on target detection in this article. It first simulates the process of recording a target in the human brain by optimizing the HMM parameters to represent the target as far as possible. And then the background clutter is defined to be the similarity, estimated by the computed model parameters, between the target and background. Finally, the newly proposed clutter metric is applied to the Search2 database, and the experiment results prove its superiority to other metrics.

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“…It consists of a number of superstates, which represent the main 1D-HMM, and each superstate consists of a number of states of the 1D-HMM model. In [11,15,22], 2D-HMMs were employed with a variation in the number of states and also the number of superstates. Fig.…”

Section: D-hmm Face Recognitionmentioning

confidence: 99%

“…The disadvantage of using pixel intensities is the associated high level of computational complexity; therefore, the tendency is to employ low-complexity methods. The discrete wavelet transform (DWT) [10], local binary pattern (LBP) [11], singular value decomposition (SVD) [12,13,14] and discrete cosine transform (DCT) [15] are the most widely methods used for dimensionality reduction.…”

Section: Related Workmentioning

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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Face verification using D-HMM and adaptive K-means clustering

Cited by 5 publications

References 19 publications

Face Recognition using Maximum Variance and SVD of Order Statistics with only Three States of Hidden Markov Model

Face Recognition using Maximum Variance and SVD of Order Statistics with only Three States of Hidden Markov Model

Hidden Markov Models for background clutter

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

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