A facial expression recognition method based on cubic spline interpolation and HOG features

Xu, Fen; Wang, Zhe

doi:10.1109/robio.2017.8324597

Cited by 8 publications

(10 citation statements)

References 22 publications

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“…In the comparison experiment, we implemented several methods by ourselves, which includes the use of SIFT [12] and HOG [11] algorithms to extract features; using SVM [16] and XGBoost, two classifiers for classification, we combine the feature extraction method and classifier for comparative experiments. The SIFT algorithm is the default parameter.…”

Section: G Comparison Experiments With Other Methodsmentioning

confidence: 99%

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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: G Comparison Experiments With Other Methodsmentioning

confidence: 99%

“…Different from the classifier that needs to consider the task type, feature extraction is more universal. Many research works have made significant achievements in feature representation, such as LBP [9], LGC [10], HOG [11] and SIFT [12].…”

Section: Introductionmentioning

confidence: 99%

ROI-Attention Vectorized CNN Model for Static Facial Expression Recognition

Sun

Zheng

2020

IEEE Access

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“…The interpolation method is one of the most commonly used for signal recovery. There are many kinds of interpolation methods, such as nearest value interpolation, cubic spline interpolation, 21 and cubic interpolation 22 …”

Section: Problem Statementmentioning

confidence: 99%

“…Cubic interpolation is a widely used method for filling some discrete points in signal and image processing, which can obtain possible approximations 21‐24 . The cubic function f ( t ) is a three‐order polynomial

f ((), t) = c_{3} t^{3} + c_{2} t^{2} + c_{1} t^{1} + c_{0}

where c 0 , c 1 , c 2 , c 3 are the undetermined coefficients.…”

Section: Problem Statementmentioning

confidence: 99%

A SDN‐based fine‐grained measurement and modeling approach to vehicular communication network traffic

Huo

Jiang

Zhu

et al. 2019

Int J Communication

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In smart cities, a large number of vehicles are connected into an intelligent transportation system and share information via the vehicular communication network (VCN). Accurate, fine-grained, and comprehensive traffic measurements are very crucial for the controller's decision making in software-defined networking (SDN) in VCN. Fine-grained traffic measurements can accurately portray network behaviors for VCN. However, this will increase a large amount of measurement overhead. Therefore, how to effectively obtain accurate and finegrained traffic is a huge challenge for VCN. To the end, this paper proposes a novel accurate and SDN-based fine-grained traffic measurement approach to obtain comprehensive traffic in VCN. Firstly, based on SDN architecture, we exploit the pull-based sampling mechanism to quickly obtain coarse-grained traffic measurement values. Secondly, based on the matrix completion theory, we use both interpolation and optimization methods to obtain fine-grained traffic measurements. Thirdly, the optimization model and detailed algorithm are proposed to attain accurate traffic. Finally, we conduct a larger number of simulations to validate the measurement approach proposed in this paper. Simulation results show that our approach exhibits better performance and is promising.

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“…The critical problem in face recognition is how to acquire facial features accurately. According to characteristics, face recognition can be divided into the following two categories: based on shallow features such as SIFT [1], LBP [2], HOG [3] and based on deep convolution neural network(DCNN [4]). The main advantage of deep learning algorithm is that it can be used with a large number of data samples for training and learning the robust face feature representation in different face recognition datasets.…”

Section: Introductionmentioning

confidence: 99%

A New Deep Neural Architecture Search Pipeline for Face Recognition

Zhu

Kou

2020

IEEE Access

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With the widespread popularity of electronic devices, the emergence of biometric technology has brought significant convenience to user authentication compared with the traditional password and mode unlocking. Among many biological characteristics, the face is a universal and irreplaceable feature with simple detection methods and good recognition accuracy. Face recognition is one of the main functions of electronic equipment propaganda. The previous work in this field mainly focused on converting loss function in traditional deep convolution neural networks without changing the network structure. With the development of AutoML, neural architecture search (NAS) has shown remarkable performance in image classification tasks. In this paper, we first propose a new deep neural architecture search pipeline combined with NAS technology and reinforcement learning strategy into face recognition. We quote the framework of NAS, which trains the child and controller networks alternately. At the same time, we optimize NAS by incorporating evaluation latency into rewards of reinforcement learning and utilize the policy gradient algorithm to search the architecture automatically with the cross-entropy loss. The network architectures we searched out have achieved state-of-the-art accuracy in the large-scale face dataset, which achieved 98.77% top-1 in the MS-Celeb-1M dataset and 99.89% in LFW dataset with relatively small network size. INDEX TERMS Neural architecture search, trainable architecture, reinforcement learning, face recognition, large-scale face dataset.

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A facial expression recognition method based on cubic spline interpolation and HOG features

Cited by 8 publications

References 22 publications

ROI-Attention Vectorized CNN Model for Static Facial Expression Recognition

ROI-Attention Vectorized CNN Model for Static Facial Expression Recognition

A SDN‐based fine‐grained measurement and modeling approach to vehicular communication network traffic

A New Deep Neural Architecture Search Pipeline for Face Recognition

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