3D facial expression recognition algorithm using local threshold binary pattern and histogram of oriented gradient

An, Shu; Ruan, Qiuqi

doi:10.1109/icsp.2016.7877838

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…AAM feature gives best result on CRF model, then U-LBP feature is combined with HOG on LDCRF with hidden state variables have shown good result. Shu An et al [2] new approach utilized to perceive to recognize the facial expression of 3D image. A new method is implemented called Local Threshold Binary Pattern (LTBP) which is related to LBP.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Approach for Facial Expression Recognition using HJDLBP and LBP Histogram in Video Sequences

Nagaral¹,

Reddy²

2018

IJIGSP

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Any kind of compassionate thoughts can't be expressed through words, but it appears on their facial expression. So, the facial expression reveals the emotions of individuals. The recognition of such emotions can be understood correctly or sometimes ambiguously from the opponent. Hence, there is a scope for automatic facial expression recognition (FER) in the context of image processing. The FER system has three different phases: face detection, feature extraction and expression classification. In face detection phase, Viola Jones face detector is used to crop the original image such that only the face region is retained by removing the unwanted region. In feature extraction stage, High-order Joint Derivative Local Binary Pattern (HJDLBP) and Local Binary Pattern (LBP) histogram algorithms are used for extracting features from the cropped image. In last stage, Support Vector machine (SVM) classifier is used in finding the precise facial expression.CK+ dataset has been used for training and testing, which consist of 442 image samples. We have considered six different universal possible expressions such as, happy, anger, disgust, fear, surprised, and sad for identification. The experimental results indicate that the overall accuracy of the proposed system was 74.8%, which is high compare to the results available in literature.

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Section: Related Workmentioning

confidence: 99%

Hybrid Approach for Facial Expression Recognition using HJDLBP and LBP Histogram in Video Sequences

Nagaral¹,

Reddy²

2018

IJIGSP

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“…This paper is not the first paper that uses the combination of both of them. Many FER systems have already used this mechanism successfully (An and Ruan, 2016;Islam et al, 2018b;2018c).…”

Section: Introductionmentioning

confidence: 99%

Fusion of Features and Extreme Learning Machine for Facial Expression Recognition

Islam

Hossain

2019

Journal of Computer Science

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Human emotion is highly correlated to facial expressions. Due to its growing demand in different sectors, an emotion recognition method is proposed through recognizing facial expressions. The input image is preprocessed and then the resulting image is segmented into four facial expression regions following the newly proposed segmentation method. Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are fused to extract the necessary features from the four segmented parts. The dimension of the feature vector is reduced using Principal Component Analysis (PCA). To classify the expressions, Extreme Learning Machine (ELM) is used. For evaluating the performance of the proposed method, three widely used and publicly available facial expression datasets (JAFFE, CK+, RaFD) are used. The proposed method achieved 95.3%, 99.84% and 98.65% accuracy while using images from JAFFE, CK+ and RaFD dataset respectively. Performance of the proposed method on these datasets is compared to other facial expression recognition methods on these datasets to indicate that the proposed method achieves state-of-the-art performance.

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A Facial Region Segmentation Based Approach to Recognize Human Emotion Using Fusion of HOG & LBP Features and Artificial Neural Network

Islam

Mahmud

Hossain

et al. 2018

2018 4th International Conference on Electrical Engineering and Information &Amp; Communication Technology (iCEEiCT)

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3D facial expression recognition algorithm using local threshold binary pattern and histogram of oriented gradient

Cited by 4 publications

References 11 publications

Hybrid Approach for Facial Expression Recognition using HJDLBP and LBP Histogram in Video Sequences

Hybrid Approach for Facial Expression Recognition using HJDLBP and LBP Histogram in Video Sequences

Fusion of Features and Extreme Learning Machine for Facial Expression Recognition

A Facial Region Segmentation Based Approach to Recognize Human Emotion Using Fusion of HOG & LBP Features and Artificial Neural Network

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