Deep peak-neutral difference feature for facial expression recognition

Chen, Jingying; Xu, Ruyi; Liu, Leyuan

doi:10.1007/s11042-018-5909-5

Cited by 28 publications

(19 citation statements)

References 40 publications

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“…Our SD-CNN method is compared with the recent state-of-the-art FER methods, including DeRL [ 12 ], FN2EN [ 18 ], FMPN [ 28 ], VGG-face [ 8 ], MicroExpNet [ 34 ], GoogLeNet [ 17 ], MultiAttention [ 24 ], DSAE [ 26 ], GCNet [ 40 ], DynamicMTL [ 41 ], IA-gen [ 20 ], CompactCNN [ 30 ], DTAGN(Joint) [ 29 ], CPPN [ 27 ], DPND [ 10 ], PPDN [ 11 ], and FAN [ 31 ]. Table 2 reports our experimental results and shows the comparisons with these methods.…”

Section: Resultsmentioning

confidence: 99%

“…The FaceNet2ExpNet achieves an accuracy of 98.6% on the small-scale CK+ dataset when following a 10-fold cross-validation protocol. Although pre-training FER networks on other face-related datasets help improve the performance, the identity information retained in the pre-trained models may have a negative impact on the accuracy of FER [ 10 ].…”

Section: Related Workmentioning

confidence: 99%

“…To address the intra-class variation issue, several studies [ 10 , 11 , 12 ] have tried to recognize facial expression by comparing a subject’s expression with the neutral expression of the same subject. In our previous work [ 10 ], a deep feature extraction network called deep peak-neutral difference is proposed to use the difference between two deep representations of the fully expressive (peak) and neutral facial expression frames for FER. The peak-neutral-difference-based methods assume that the images with neutral and peak expressions can be discriminated accurately from the facial expression sequences.…”

Section: Introductionmentioning

confidence: 99%

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Self-Difference Convolutional Neural Network for Facial Expression Recognition

Liu

Jiang

Huo

et al. 2021

Sensors

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Facial expression recognition (FER) is a challenging problem due to the intra-class variation caused by subject identities. In this paper, a self-difference convolutional network (SD-CNN) is proposed to address the intra-class variation issue in FER. First, the SD-CNN uses a conditional generative adversarial network to generate the six typical facial expressions for the same subject in the testing image. Second, six compact and light-weighted difference-based CNNs, called DiffNets, are designed for classifying facial expressions. Each DiffNet extracts a pair of deep features from the testing image and one of the six synthesized expression images, and compares the difference between the deep feature pair. In this way, any potential facial expression in the testing image has an opportunity to be compared with the synthesized “Self”—an image of the same subject with the same facial expression as the testing image. As most of the self-difference features of the images with the same facial expression gather tightly in the feature space, the intra-class variation issue is significantly alleviated. The proposed SD-CNN is extensively evaluated on two widely-used facial expression datasets: CK+ and Oulu-CASIA. Experimental results demonstrate that the SD-CNN achieves state-of-the-art performance with accuracies of 99.7% on CK+ and 91.3% on Oulu-CASIA, respectively. Moreover, the model size of the online processing part of the SD-CNN is only 9.54 MB (1.59 MB ×6), which enables the SD-CNN to run on low-cost hardware.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Self-Difference Convolutional Neural Network for Facial Expression Recognition

Liu

Jiang

Huo

et al. 2021

Sensors

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“…It extracts only expression-specific features from a face image, and explores the presentation order of the samples during training. A more powerful facial feature method called deep peak-neutral difference has also been proposed [46]. This difference is defined as the difference between two deep representations of the fully expressive (peak) and neutral facial expression frames, where unsupervised clustering and semi-supervised classification methods automatically obtain the neutral and peak frames from the expression sequence.…”

Section: Face Images For Facial Expression Recognitionmentioning

confidence: 99%

Sample awareness-based personalized facial expression recognition

Wen

2019

Appl Intell

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The behavior of the current emotion classification model to recognize all test samples using the same method contradicts the cognition of human beings in the real world, who dynamically change the methods they use based on current test samples. To address this contradiction, this study proposes an individualized emotion recognition method based on context awareness. For a given test sample, a classifier that was deemed the most suitable for the current test sample was first selected from a set of candidate classifiers and then used to realize the individualized emotion recognition. The Bayesian learning method was applied to select the optimal classifier and then evaluate each candidate classifier from the global perspective to guarantee the optimality of each candidate classifier. The results of the study validated the effectiveness of the proposed method.

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“…In essence, expression feature extraction refers to the conversion of a high-dimensional facial expression image vector into a lowdimensional vector with a lot of distinguished information. Many algorithms used to extract image features are commonly applied to facial expression recognition [4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Facial Expression Recognition Using Kernel Entropy Component Analysis Network and DAGSVM

Chen

et al. 2021

Complexity

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Facial expression recognition (FER) plays a significant part in artificial intelligence and computer vision. However, most of facial expression recognition methods have not obtained satisfactory results based on low-level features. The existed methods used in facial expression recognition encountered the major issues of linear inseparability, large computational burden, and data redundancy. To obtain satisfactory results, we propose an innovative deep learning (DL) model using the kernel entropy component analysis network (KECANet) and directed acyclic graph support vector machine (DAGSVM). We use the KECANet in the feature extraction stage. In the stage of output, binary hashing and blockwise histograms are adopted. We sent the final output features to the DAGSVM classifier for expression recognition. We test the performance of our proposed method on three databases of CK+, JAFFE, and CMU Multi-PIE. According to the experiment results, the proposed method can learn high-level features and provide more recognition information in the stage of training, obtaining a higher recognition rate.

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Deep peak-neutral difference feature for facial expression recognition

Cited by 28 publications

References 40 publications

Self-Difference Convolutional Neural Network for Facial Expression Recognition

Self-Difference Convolutional Neural Network for Facial Expression Recognition

Sample awareness-based personalized facial expression recognition

Facial Expression Recognition Using Kernel Entropy Component Analysis Network and DAGSVM

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