Fully automatic 3D facial expression recognition using polytypic multi-block local binary patterns

Liu, Yuanyuan; Ruan, Qiuqi; Jin, Yi; An, Gaoyun; Zhao, Ruizhen

doi:10.1016/j.sigpro.2014.09.033

Cited by 36 publications

(16 citation statements)

References 45 publications

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“…G. He & Sang, 2013) have been improved. Other variants include multi-scale approaches (Xiaofei, et al, 2013) (Li, et al, 2015), feature selection methods (Girish, et al, 2014), and noise-resistant approaches (Chen, et al, 2013) (Ren, Jiang, & Yuan, 2013) (Ziqi, et al, 2014). LBP has been also combined with other texture descriptors (Shufu, Shiguang, Xilin, & Wen, 2008) (Zhe, Hong, Yueliang, & Tao, 2012) (M.…”

Section: Discussionmentioning

confidence: 99%

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Efficient data mining for local binary pattern in texture image analysis

Kwak

Wood

2015

Expert Systems with Applications

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Local binary pattern (LBP) is a simple gray scale descriptor to characterize the local distribution of the grey levels in an image. Multi-resolution LBP and/or combinations of the LBPs have shown to be effective in texture image analysis. However, it is unclear what resolutions or combinations to choose for texture analysis. Examining all the possible cases is impractical and intractable due to the exponential growth in a feature space. This limits the accuracy and time- and space-efficiency of LBP. Here, we propose a data mining approach for LBP, which efficiently explores a high-dimensional feature space and finds a relatively smaller number of discriminative features. The features can be any combinations of LBPs. These may not be achievable with conventional approaches. Hence, our approach not only fully utilizes the capability of LBP but also maintains the low computational complexity. We incorporated three different descriptors (LBP, local contrast measure, and local directional derivative measure) with three spatial resolutions and evaluated our approach using two comprehensive texture databases. The results demonstrated the effectiveness and robustness of our approach to different experimental designs and texture images.

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Section: Discussionmentioning

confidence: 99%

“…In MBLTP (Xiaofei, et al, 2013), LTP is computed based on the average value of blocks. Polytypic multi-block LBP (P-MLBP) (Li, Ruan, Jin, An, & Zhao, 2015) fuses depth and texture information to represent three-dimensional facial expressions.…”

Section: Local Binary Pattern and Its Variantsmentioning

confidence: 99%

Efficient data mining for local binary pattern in texture image analysis

Kwak

Wood

2015

Expert Systems with Applications

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“…The first one classifies facial expressions with the popular Local Binary Patterns (LBP) [10][11][12][13] features extracted from original clean images without any corruption. In this case, according to the normalized value of the eye distance, a resized image of 110×150 pixels was cropped from original images before performing LBP operators, as done in [10].…”

Section: Experiments Setupmentioning

confidence: 99%

Learning a Class-Specific Dictionary for Facial Expression Recognition

Zhang

Cui

et al. 2016

Cybernetics and Information Technologies

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“…In recent years, the human-computer interaction challenge has led to the demand to introduce efficient facial and speech recognition systems Chao et al (2015); Danisman et al (2013); Ververidis and Kotropoulos (2008); Li et al (2015); Gupta et al (2015). Facial emotion recognition is the identification of a human emotion based on the facial expression and mimics Zhang et al (2016).…”

Section: Introductionmentioning

confidence: 99%

“…The facial emotion recognition has a wide range of appliction prospects in different areas, such as medicine Zhao et al (2014), robotics Shih et al (2008);P. et al (2015), computer vision, surveillance systems Chao et al (2015), machine learning Cruz et al (2014), artificial intelligence, communication Latif et al (2015); Sudha et al (2015), psychological studies Li et al (2015Li et al ( ), smart vehicles P. et al (2015, security and embedded systems Sun and An (2010).…”

Section: Introductionmentioning

confidence: 99%

Facial emotion recognition using Min-Max similarity classifier

Krestinskaya

James

2017

Preprint

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Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally simple feature selection and classification technique for emotion recognition is still an open problem. In this paper, we propose an efficient and straightforward facial emotion recognition algorithm to reduce the problem of inter-class pixel mismatch during classification. The proposed method includes the application of pixel normalization to remove intensity offsets followed-up with a Min-Max metric in a nearest neighbor classifier that is capable of suppressing feature outliers. The results indicate an improvement of recognition performance from 92.85% to 98.57% for the proposed Min-Max classification method when tested on JAFFE database. The proposed emotion recognition technique outperforms the existing template matching methods.

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Fully automatic 3D facial expression recognition using polytypic multi-block local binary patterns

Cited by 36 publications

References 45 publications

Efficient data mining for local binary pattern in texture image analysis

Efficient data mining for local binary pattern in texture image analysis

Learning a Class-Specific Dictionary for Facial Expression Recognition

Facial emotion recognition using Min-Max similarity classifier

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