Facial expression recognition using a combination of enhanced local binary pattern and pyramid histogram of oriented gradients features extraction

Sharifnejad, Maede; Shahbahrami, Asadollah; Akoushideh, Alireza; Hassanpour, Reza

doi:10.1049/ipr2.12037

Cited by 11 publications

(5 citation statements)

References 33 publications

(45 reference statements)

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“…Compared to the traditional feature extraction method, Refs. [11][12][13][14] used the hybrid feature extraction method, which significantly promoted the recognition accuracy. Due to the limitations of traditional methods, the extracted feature information was not comprehensive enough and it was necessary to add auxiliary measures to improve the recognition accuracy.…”

Section: Methods Ck+ MMImentioning

confidence: 99%

“…This process is related to the correct recognition rate of facial expressions. In feature extraction, there are mainly active appearance models (AAM), which are based on the localization of facial feature points, and local feature extraction/algorithms, such as Gabor wavelets, local binary patterns (LBP) [9], and multi-feature fusion [10,11], etc. Traditional feature extraction approaches in facial expression recognition applications can also be detected in [12,13].…”

Section: Introductionmentioning

confidence: 99%

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The Facial Expression Data Enhancement Method Induced by Improved StarGAN V2

Han

2023

Symmetry

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Due to the small data and unbalanced sample distribution in the existing facial emotion datasets, the effect of facial expression recognition is not ideal. Traditional data augmentation methods include image angle modification, image shearing, and image scrambling. The above approaches cannot solve the problem that is the high similarity of the generated images. StarGAN V2 can generate different styles of images across multiple domains. Nevertheless, there are some defects in gener-ating these facial expression images, such as crooked mouths and fuzzy facial expression images. To service such problems, we improved StarGAN V2 by solving the drawbacks of creating pictures that apply an SENet to the generator of StarGAN V2. The generator’s SENet can concentrate at-tention on the important regions of the facial expression images. Thus, this makes the generated symmetrical expression image more obvious and easier to distinguish. Meanwhile, to further im-prove the quality of the generated pictures, we customized the hinge loss function to reconstruct the loss functions that increase the boundary of real and fake images. The created facial expression pictures testified that our improved model could solve the defects in the images created by the original StarGAN V2. The experiments were conducted on the CK+ and MMI datasets. The correct recognition rate of the facial expressions on the CK+ was 99.2031%, which is a 1.4186% higher accuracy than that of StarGAN V2. The correct recognition rate of the facial expressions on the MMI displays was 98.1378%, which is 5.059% higher than that of the StarGAN V2 method. Furthermore, contrast test outcomes proved that the improved StarGAN V2 performed better than most state-of-the-art methods.

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Section: Methods Ck+ MMImentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Facial Expression Data Enhancement Method Induced by Improved StarGAN V2

Han

2023

Symmetry

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“…However, the extensibility of this method is vulnerable. Moreover, Sharifnejad proposed an expression recognition algorithm based on a pyramid histogram for gradient feature extraction [39]. The pyramid based face image can be constructed by multi-scale analysis, which can effectively extract the global and local features of the face.…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

Face Recognition in Single Sample Per Person Fusing Multi-Scale Features Extraction and Virtual Sample Generation Methods

Yuan

Zhang

et al. 2022

Front. Appl. Math. Stat.

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Although face recognition has received a lot of attention and development in recent years, it is one of the research hotspots due to the low efficiency of Single Sample Per Person (SSPP) information in face recognition. In order to solve this problem, this article proposes a face recognition method based on virtual sample generation and multi-scale feature extraction. First, in order to increase the training sample information, a new NMF-MSB virtual sample generation method is proposed by combining the Non-negative Matrix Factorization (NMF) reconstruction strategy with Mirror transform(M), Sliding window(S), and Bit plane(B) sample extension methods. Second, a feature extraction method (named WPD-HOG-P) based on Wavelet Packet Decomposition, Histograms of Oriented Gradients, and image Pyramid is proposed. The proposed WPD-HOG-P method is beneficial to multi-scale facial image feature extraction. Finally, based on the extracted WPD-HOG-P features, the recognition model is established by using a grid search optimization support vector machine. Experimental results on ORL and FERET data sets show that the proposed method has higher recognition rates and lower computational complexity than the benchmark methods.

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“…Facial expressions are a more visual and intuitive alternative to language to express people's inner emotions [1]. With the rapid advance of the artificial intelligence technology, facial expression recognition has drawn wide attention from researchers working on image processing [2], and has found a broad wide range of applications in human–computer interaction [3], safe driving [4], and online education [5]. For example, in the field of safe driving, the driver's facial expression information can be obtained in real time by the camera to check for proper driving, so as to help avoid unexpected accidents; in the field of education, the class's status can be evaluated by identifying the students' facial expressions, based on which the teachers can adjust their teaching methods to improve outcomes.…”

Section: Introductionmentioning

confidence: 99%

Facial expression recognition based on improved residual network

Zhang

Tang

2023

IET Image Processing

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Facial expressions are an important part of human emotional signals and their recognition has become an important topic of research in the field of pattern recognition. Deep learning based methods have achieved great success in the recognition of facial expressions. However, with the evolution of convolution neural networks and the increased network depth, these methods suffer from problems such as degraded network performance and loss of feature information. To address these problems, a novel facial expression recognition algorithm based on an improved residual neural network is proposed. First, a residual neural network is designed to extract deep features while retaining the shallow ones. This can effectively prevent the degradation of network performance. Moreover, when the gradient of the Rectified Linear Units activation function used in the residual module is 0, it will inactivate the neurons and cause a loss of feature information. To address this, the Mish activation function is used instead. The slight allowance for negative values in Mish improves the gradient flow. Next, an inception module is introduced to obtain richer feature information under the same receptive field. Finally, by conducting verification experiments on the public datasets CK+ and KDEF, the authors manage to solve the problems of degraded network performance and insufficient information from extracted features, achieving recognition accuracy rates of 96.37% and 93.38% on the two datasets, respectively.

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Facial expression recognition using a combination of enhanced local binary pattern and pyramid histogram of oriented gradients features extraction

Cited by 11 publications

References 33 publications

The Facial Expression Data Enhancement Method Induced by Improved StarGAN V2

The Facial Expression Data Enhancement Method Induced by Improved StarGAN V2

Face Recognition in Single Sample Per Person Fusing Multi-Scale Features Extraction and Virtual Sample Generation Methods

Facial expression recognition based on improved residual network

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