Facial expression recognition using LBP template of facial parts and multilayer neural network

Kauser, Nazima; Sharma, Jitendra

doi:10.1109/i-smac.2017.8058389

Cited by 21 publications

(8 citation statements)

References 9 publications

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“…In terms of traditional manual features, in 2016, Zhou et al [12] used the SIFT feature to represent the pixel features of face images, and they proposed a feature alignment scheme based on SIFT to achieve the automatic alignment and matching of image features. In 2017, Kauser and Sharma et al [21] proposed that when extracting the entire face image with LBP, the specific position of each feature point cannot be distinguished. In their work, LBP was used to extract the features of several important parts of the face, and then these features were connected to form feature vectors for input to neural networks.…”

Section: A Traditional Manual Featuresmentioning

confidence: 99%

ROI-Attention Vectorized CNN Model for Static Facial Expression Recognition

Sun

Zheng

2020

IEEE Access

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When using neural networks to recognize facial expressions, determining which features help identify different expressions is essential, and there is a massive information transmission loss between layers of network with multiple layers. This paper proposes a robust vectorized convolutional neural network (CNN) model that introduces an attention mechanism for extracting features in the region of interests(ROIs) of the face. The ROIs in the facial image are marked before the image is input into the neural network. In particular, the attention concept is adopted in the first layer of the proposed neural network to perform ROIs-related convolution calculation, and ROIs-related convolution calculation results of the specific fields in the ROIs are increased by extracting more robust features. Next, the idea of features' vectors inspired by CapsNet is used in the following layer of the proposed neural network. Multi-level convolutions are used to extract feature vectors of different ROIs for facial expression, and then the feature vectors are reconstructed by a decoder to reconstruct the image. Comprehensive comparative experiments and cross-database experiments are conducted to verify the validity and robustness of our proposed model. The experimental results also demonstrate that our method is very effective in improving the performance of facial expressions recognition. INDEX TERMS Regions of interests, attention mechanism, facial expression recognition, vectored features, CapsNet.

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Section: A Traditional Manual Featuresmentioning

confidence: 99%

ROI-Attention Vectorized CNN Model for Static Facial Expression Recognition

Sun

Zheng

2020

IEEE Access

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“…One of the interesting feature category is appearance features, which include the texture patterns like LBP (CaifengShan et.al. [9], Nazima Kauser & Jitendra Sharma [10] and LTP and its variants. Another remarkable feature used is the Histogram of oriented directional gradients (HOG) which helps in getting the idea of the shape of the edges and thus much useful in FER (Carcagnì.…”

Section: Previous Related Approachesmentioning

confidence: 99%

“…They include Gabor Features [1]. Histogram of Oriented Gradients (HOG) [2], Local Binary Patterns (LBP) [3], Local Ternary Patterns (LTP) [4]and also their variants like GLTP (Gradient LTP) [5] etc. Motion features like Motion History Image (MHI) [6] are also well used to know the dynamics of the gestures to understand the emotion expressed.…”

Section: Introductionmentioning

confidence: 99%

Region based Facial Expression Recognition using Gradient Directions

C.*,

Devi,

et al. 2020

IJITEE

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Facial Expression Recognition (FER) has gained significant importance in the research field of Affective Computing in different extents. As a part of the different dimensional thinking, aiming at improving the accuracy of the recognition system and reducing the computational load, region based FER is proposed in this paper. The system is an emotion identifying system among the basic emotions, through subject independent template matching based on gradient directions. The model designed is tested on the Enhanced Cohn-Kanade (CK+) dataset. Another important contribution of the work is using only eye (including eyebrows and the nose portion near eyes) and mouth regions in the emotion recognition. The emotion classification result is 94.3% (CK+ dataset) for 6-class FER.

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“…Finally, SVMs are used for the classification purposes. A Similar technique has been adopted in [13], where, initially, faces are detected using a Viola-Jones face detector followed by a feature extraction phase where Local Binary Pattern (LBP) features are used for representation purposes.…”

Section: Related Workmentioning

confidence: 99%

Empirical Evaluation of SVM for Facial Expression Recognition

Saeed¹,

Baber²,

Bakhtyar³

et al. 2018

ijacsa

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Support Vector Machines (SVMs) have shown better generalization and classification capabilities in different applications of computer vision; SVM classifies underlying data by a hyperplane that can separate the two classes by maintaining the maximum margin between the support vectors of the respective classes. An empirical analysis of SVMs on the facial expression recognition task is reported with high intra and low inter class variations by conducting an extensive set of experiments on a large-scale Fer 2013 dataset. Three different kernel functions of SVM are used; linear kernel, quadratic kernel and cubic kernel, whereas, Histogram of Oriented Gradient (HoG) is used as a feature descriptor. Cubic Kernel achieves highest accuracy on Fer 2013 dataset using HoG.

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Facial expression recognition using LBP template of facial parts and multilayer neural network

Cited by 21 publications

References 9 publications

ROI-Attention Vectorized CNN Model for Static Facial Expression Recognition

ROI-Attention Vectorized CNN Model for Static Facial Expression Recognition

Region based Facial Expression Recognition using Gradient Directions

Empirical Evaluation of SVM for Facial Expression Recognition

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