Facial Expression Recognition Based on Gabor Wavelet Transformation and Elastic Templates Matching

Zhan, Yongzhao; Ye, Jingfu; Niu, Dejiao; Cao, Peng

doi:10.1142/s0219467806002112

Cited by 18 publications

(6 citation statements)

References 9 publications

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“…Gabor wavelet is very similar to the cell's human visual system which has nice properti extraction in local spatial and frequency dom the good directional selectivity, multi-scale with light, so it has been widely used in imag pattern recognition [12]. Gabor wavelet windowed Fourier Transform.…”

Section: A Gabor Wavelet Transformmentioning

confidence: 99%

Facial expression recognition based on Gabor Wavelet transform and Histogram of Oriented Gradients

Quan

Ren

2015

2015 IEEE International Conference on Mechatronics and Automation (ICMA)

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In order to get more effective expression features, this paper proposes an approach based on Gabor feature and Histogram of Oriented Gradients (HOG). Gabor Wavelet filter is first used as preprocessing stage for feature extraction. Handing the characteristics with a large number of dimensions, binary encoding (BC) is applied for dimensionality reduction. Dimensionality of the feature vector is reduced by using HOG algorithm. Experiments were performed on Cohn-Kanade facial expression database and the support vector machine classifier is used for expression classification. We obtained experimental results with an average recognition rate of 92.5%, which reveals that the proposed method is superior to other Gabor Wavelet transform based approaches under the same experimental environment.

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Section: A Gabor Wavelet Transformmentioning

confidence: 99%

Facial expression recognition based on Gabor Wavelet transform and Histogram of Oriented Gradients

Quan

Ren

2015

2015 IEEE International Conference on Mechatronics and Automation (ICMA)

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“…This representation is called the Gauss–Laguerre transform [16]. For a given image I ( x , y ) ∈ L 2 ( R 2 , dx 2 ), the expression

I_{p} false(r, θ false) = I ()(, \tilde{x} + r \sin θ, \tilde{y} + r \cos θ)

is the representation in the polar coordinate space centred at the pivot

x ()(, \tilde{x}, \tilde{y})

[23]. I p (.)…”

Section: Data Processing: Hyper‐spectral Analysismentioning

confidence: 99%

Facial biometrics for situational awareness systems

et al. 2013

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This study contributes to developing the concept of decision-making support in biometric-based situational awareness systems. Such systems assist users in gathering and analysing biometric data, and support the decision-making on the human behavioural pattern and/or authentication. As an example, the authors consider a facial biometric assistant that functions based on multi-spectral biometrics in visible and infrared bands; it involves facial expression recognition, face recognition in both spectra, as well as estimation of physiological parameters. The authors also investigate usage of facial biometrics for the semantic representation for advanced decision-making.

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“…In order to extract expression features in all positions of an image accurately, we lattice the whole expression image by a predefined grid with a fixed size of 5 × 5 pixels [16] .…”

Section: Dependency Set Z Selectionmentioning

confidence: 99%

A New Classifier for Facial Expression Recognition: Fuzzy Buried Markov Model

Zhan

Cheng

Chen

et al. 2010

J. Comput. Sci. Technol.

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Zhan YZ, Cheng KY, Chen YB et al. A new classifier for facial expression recognition: Fuzzy buried Markov model. Abstract To overcome the disadvantage of classical recognition model that cannot perform well enough when there are some noises or lost frames in expression image sequences, a novel model called fuzzy buried Markov model (FBMM) is presented in this paper. FBMM relaxes conditional independence assumptions for classical hidden Markov model (HMM) by adding the specific cross-observation dependencies between observation elements. Compared with buried Markov model (BMM), FBMM utilizes cloud distribution to replace probability distribution to describe state transition and observation symbol generation and adopts maximum mutual information (MMI) method to replace maximum likelihood (ML) method to estimate parameters. Theoretical justifications and experimental results verify higher recognition rate and stronger robustness of facial expression recognition for image sequences based on FBMM than those of HMM and BMM.

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Facial Expression Recognition Based on Gabor Wavelet Transformation and Elastic Templates Matching

Cited by 18 publications

References 9 publications

Facial expression recognition based on Gabor Wavelet transform and Histogram of Oriented Gradients

Facial expression recognition based on Gabor Wavelet transform and Histogram of Oriented Gradients

Facial biometrics for situational awareness systems

A New Classifier for Facial Expression Recognition: Fuzzy Buried Markov Model

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