Interval graph of facial regions with common intersection salient points for identifying and classifying facial expression

Avani, V. Suma; Shaila, S. G.; Vadivel, A.

doi:10.1007/s11042-020-09806-5

Cited by 5 publications

(2 citation statements)

References 45 publications

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“…Grouping of the facial features as handcrafted features and learning‐based features is performed. The former ones need an elaborate human design and selection, 22–24 while the latter ones denote the higher‐level abstractions that are extracted using the deep learning techniques. These features are found to be highly vigorous to the variations in the facial positions and scale in FER task, than the handcrafted features 25 .…”

Section: Related Workmentioning

confidence: 99%

Fused deep learning based Facial Expression Recognition of students in online learning mode

Sumalakshmi

Vasuki²

2022

Concurrency and Computation

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In this research work, Facial Expression Recognition (FER) is used in the analysis of facial expressions during the online learning sessions in the prevailing pandemic situation. An integrated geometric and appearance feature extraction is presented for the FER of the students participating in the online classes. The integrated features provided a low-dimensional significant feature area for better facial data representation. Feasible Weighted Squirrel Search Optimization (FW-SSO) algorithm is applied for selecting the optimal features due to its efficient exploration of the search space and enhancement of the dynamic search. The output of the FW-SSO algorithm is used for tuning the autoencoder. Autoencoder is used for combining the G&A features, for feature optimization process. Classification is done by using Long Short-Term Memory (LSTM) network with Attention Mechanism (ALSTM), as it is highly efficient in capturing the long-term dependency of the facial landmarks in the image/video sequences.The proposed fused deep learning method focuses on the fusion of the G&A features for high discrimination. Experimental analysis using FER-2013 and LIRIS datasets demonstrated that the proposed method achieved maximum accuracy of 85.96% than the existing architectures and maximum accuracy of 88.24% than the VGGNet-CNN architecture.

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Section: Related Workmentioning

confidence: 99%

Fused deep learning based Facial Expression Recognition of students in online learning mode

Sumalakshmi

Vasuki²

2022

Concurrency and Computation

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“…The features are grouped into handcrafted features and learning-based features. Handcrafted features usually require human design and selection elaborately [17], [18], [19]. Learning-based features refer to the high-level abstractions extracted with deep learning techniques.…”

Section: A Facial Expression Recognition Systemmentioning

confidence: 99%

Facial Expression Recognition Using Hybrid Features of Pixel and Geometry

Liu

Hirota

et al. 2021

IEEE Access

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Facial Expression Recognition (FER) has long been a challenging task in the field of computer vision. Most of the existing FER methods extract facial features on the basis of face pixels, ignoring the relative geometric position dependencies of facial landmark points. This article presents a hybrid feature extraction network to enhance the discriminative power of emotional features. The proposed network consists of a Spatial Attention Convolutional Neural Network (SACNN) and a series of Long Short-term Memory networks with Attention mechanism (ALSTMs). The SACNN is employed to extract the expressional features from static face images and the ALSTMs is designed to explore the potentials of facial landmarks for expression recognition. A deep geometric feature descriptor is proposed to characterize the relative geometric position correlation of facial landmarks. The landmarks are divided into seven groups to extract deep geometric features, and the attention module in ALSTMs can adaptively estimate the importance of different landmark regions. By jointly combining SACNN and ALSTMs, the hybrid features are obtained for expression recognition. Experiments conducted on three public databases, FER2013, CK+, and JAFFE, demonstrate that the proposed method outperforms the previous methods, with the accuracies of 74.31%, 95.15%, and 98.57%, respectively. The preliminary results of Emotion Understanding Robot System (EURS) indicate that the proposed method has the potential to improve the performance of human-robot interaction.

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Facial expression recognition based on strong attention mechanism and residual network

Qian

Mu²,

Tian³

et al. 2022

Multimed Tools Appl

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Interval graph of facial regions with common intersection salient points for identifying and classifying facial expression

Cited by 5 publications

References 45 publications

Fused deep learning based Facial Expression Recognition of students in online learning mode

Fused deep learning based Facial Expression Recognition of students in online learning mode

Facial Expression Recognition Using Hybrid Features of Pixel and Geometry

Facial expression recognition based on strong attention mechanism and residual network

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