Improved Facial Expression Recognition Based on DWT Feature for Deep CNN

Bendjillali, Ridha Ilyas; Beladgham, Mohammed; Merit, Khaled; Taleb‐Ahmed, Abdelmalik

doi:10.3390/electronics8030324

Cited by 62 publications

(34 citation statements)

References 33 publications

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“…In [53], different levels of deep learning features extracted from the SIFT and CNN models were combined, and finally, the SVM was used to classify the mixed features. In [54], the Viola-jones method [13] was used to locate the face, and contrast limited adaptive histogram equalization (CLAHE) was used to enhance the face. Then, discrete wavelet transform (DWT) was used to extract the facial features, and finally, the extracted features are used to train the CNN network.…”

Section: The Fusion Of Traditional Methods and Convolutional Neuramentioning

confidence: 99%

“…The comparison results of recognition accuracy on the Fer2013, CK+, FER+ and RAF data sets are listed in Table IV, Table V, Table VI and Table VII, respectively. [55] 68.79% DenseNet [25] 71.02% GoogLeNet [25] 65.76% VGG-Face [25] 69.18% Ref [51] 61.86% Ref [29] 65.03% Ref [30] 68% Ref [31] 66% Ref [34] 70.02% AlexNet [42] 66.67% VGGNet [42] 69.41% ResNet [42] 70.74% MBCC-CNN(ours) 71.52% [46] 97.02% Ref [53] 94.82% Ref [23] 90.48% Ref [28] 97.38% Ref [51] 97.35% Ref [33] 94.67% Ref [34] 98% WMCNN-LSTM [36] 97.50% Ref [35] 87.20% Ref [54] 96.46% Ref [37] 98.38% Ref [44] 96.15% Ref [47] 96.46% Ref [61] 96.28% MBCC-CNN(ours) 98.48% TableVI THE ACCURACY COMPARISON RESULTS ON THE FER+ DATA SET Method Accuracy DenseNet [62] 86.54% Ref [63] 85.67% Ref [64] 87.15% Ref [65] 85.10% Ref [66] 82.00% Ref [67] 84.29% Ref [68] 87.76% ResNet50 (transfer learning) [69] 79.90% MBCC-CNN(ours)…”

Section: F Comparison Of Recognition Accuracy With Other Methodsmentioning

confidence: 99%

“…The recognition rate of this method on the CK+ data set is 94.82%. In reference [54], viola-jones is used to locate the face, and contrast limited adaptive histogram equalization (CLAHE) is adopted to enhance the face. Then, DWT is used to extract face features, which are used to train the CNN network.…”

Section: F Comparison Of Recognition Accuracy With Other Methodsmentioning

confidence: 99%

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A Facial Expression Recognition Method Based on a Multibranch Cross-Connection Convolutional Neural Network

2021

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This paper proposes a new method based on a multiple branch cross-connected convolutional neural network (MBCC-CNN) for facial expression recognition. Compared with traditional machine learning methods, the proposed method can extract image features more effectively. In addition, in contrast to single-structure convolutional neural networks, the MBCC-CNN model is constructed based on the residual connection, Network in Network, and tree structure approaches together. It also adds a shortcut cross connection for the summation of the convolution output layer, which makes the data flow between networks more smooth, improves the feature extraction ability of each receptive field. The proposed method can fuse the features of each branches more effectively, which solves the problem of insufficient feature extraction of each branches and increases the recognition performance. The experimental results based on the Fer2013, CK+, FER+ and RAF data sets show that the recognition rates of the proposed MBCC-CNN method are 71.52%, 98.48%, 88.10% and 87.34%, respectively. Compared with some most recently work, the proposed method can provide better facial expression recognition performance and has good robustness. INDEX TERMS Facial expression recognition; Convolutional neural network; Residual connection; Network in Network; Robustness.

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Section: The Fusion Of Traditional Methods and Convolutional Neuramentioning

confidence: 99%

Section: F Comparison Of Recognition Accuracy With Other Methodsmentioning

confidence: 99%

Section: F Comparison Of Recognition Accuracy With Other Methodsmentioning

confidence: 99%

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A Facial Expression Recognition Method Based on a Multibranch Cross-Connection Convolutional Neural Network

2021

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“…This method is mainly adopted on clipping the histogram at a predefined value in order to limit contrast amplification before computing the CDF value this method is called the clip limit [16]. The method is fast, easy to implement, and fully automatic [17].…”

Section: Contrast Limited Adaptive Histogram Equalization (Clahe)mentioning

confidence: 99%

Illumination-robust face recognition based on deep convolutional neural networks architectures

Bendjillali¹,

Beladgham²,

Merit³

et al. 2020

IJEECS

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<p><span>In the last decade, facial recognition techniques are considered the most important fields of research in biometric technology. In this research paper, we present a Face Recognition (FR) system divided into three steps: The Viola-Jones face detection algorithm, facial image enhancement using Modified Contrast Limited Adaptive Histogram Equalization algorithm (M-CLAHE), and feature learning for classiﬁcation. For learning the features followed by classiﬁcation we used VGG16, ResNet50 and Inception-v3 Convolutional Neural Networks (CNN) architectures for the proposed system. Our experimental work was performed on the Extended Yale B database and CMU PIE face database. Finally, the comparison with the other methods on both databases shows the robustness and effectiveness of the proposed approach. Where the Inception-v3 architecture has achieved a rate of 99, 44% and 99, 89% respectively.</span></p>

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“…It is worth mentioning, finally, that polarity and sentiment detection were also exploited in face analysis [94][95][96][97][98][99], posture analysis [100], brain signals [101], and the behavioral analysis of groups of people [102]. These methodologies were tailored to specific tasks and strictly related to the video domain.…”

Section: Sentiment Analysis: Other Applicationsmentioning

confidence: 99%

A Survey on Deep Learning in Image Polarity Detection: Balancing Generalization Performances and Computational Costs

et al. 2019

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Deep convolutional neural networks (CNNs) provide an effective tool to extract complex information from images. In the area of image polarity detection, CNNs are customarily utilized in combination with transfer learning techniques to tackle a major problem: the unavailability of large sets of labeled data. Thus, polarity predictors in general exploit a pre-trained CNN as the feature extractor that in turn feeds a classification unit. While the latter unit is trained from scratch, the pre-trained CNN is subject to fine-tuning. As a result, the specific CNN architecture employed as the feature extractor strongly affects the overall performance of the model. This paper analyses state-of-the-art literature on image polarity detection and identifies the most reliable CNN architectures. Moreover, the paper provides an experimental protocol that should allow assessing the role played by the baseline architecture in the polarity detection task. Performance is evaluated in terms of both generalization abilities and computational complexity. The latter attribute becomes critical as polarity predictors, in the era of social networks, might need to be updated within hours or even minutes. In this regard, the paper gives practical hints on the advantages and disadvantages of the examined architectures both in terms of generalization and computational cost.

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Improved Facial Expression Recognition Based on DWT Feature for Deep CNN

Cited by 62 publications

References 33 publications

A Facial Expression Recognition Method Based on a Multibranch Cross-Connection Convolutional Neural Network

A Facial Expression Recognition Method Based on a Multibranch Cross-Connection Convolutional Neural Network

Illumination-robust face recognition based on deep convolutional neural networks architectures

A Survey on Deep Learning in Image Polarity Detection: Balancing Generalization Performances and Computational Costs

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