Facial expression recognition based on dual-feature fusion and improved random forest classifier

Yang, Biao; Cao, Jinmeng; Jiang, Dequan; Lv, Jidong

doi:10.1007/s11042-017-5489-9

Cited by 25 publications

(11 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Random Forests model (abbreviated as RF) [19] is one of the most popular machine-learning algorithms available today. Due to the advantages such as simple-structure, easy-implementation [19], anti-overfitting nature [20], etc., RF has been widely used in many fields, such as image recognition [21], geography [22], economics [23], manufacturing [24], agriculture [25] and nanomaterials [26]. Compared to ANN and SVR, RF has a deeper model structure and works better for datasets with steep-manifold characteristic [27].…”

Section: Introductionmentioning

confidence: 99%

Prediction and Knowledge Mining of Outdoor Atmospheric Corrosion Rates of Low Alloy Steels Based on the Random Forests Approach

Zhi

Zhang

et al. 2019

Metals

View full text Add to dashboard Cite

The objective of this paper is to develop an approach to forecast the outdoor atmospheric corrosion rate of low alloy steels and do corrosion-knowledge mining by using a Random Forests algorithm as a mining tool. We collected the corrosion data of 17 low alloy steels under 6 atmospheric corrosion test stations in China over 16 years as the experimental datasets. Based on the datasets, a Random Forests model is established to implement the purpose of the corrosion rate prediction and data-mining. The results showed that the random forests can achieve the best generalization results compared to the commonly used machine learning methods such as the artificial neural network, support vector regression, and logistic regression. In addition, the results also showed that regarding the effect to the corrosion rate, environmental factors contributed more than chemical compositions in the low alloy steels, but as exposure time increases, the effect of the environmental factors will gradually become less. Furthermore, we give the effect changes of six environmental factors (Cl− concentration, SO2 concentration, relative humidity, temperature, rainfall, and pH) on corrosion with exposure time increasing, and the results illustrated that pH had a significant contribution to the corrosion of the entire process. The paper also dealt with the problem of the corrosion rate forecast, especially for changing environmental factors situations, and obtained the qualitative and quantitative results of influences of each environmental factor on corrosion.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction and Knowledge Mining of Outdoor Atmospheric Corrosion Rates of Low Alloy Steels Based on the Random Forests Approach

Zhi

Zhang

et al. 2019

Metals

View full text Add to dashboard Cite

show abstract

“…This section describes three different types of datasets for the facial expression recognition namely JAFFE dataset, 37 FER‐2013 dataset 38 and CK + dataset 39 . The details regarding the datasets are described in the following section.…”

Section: Proposed Methodologymentioning

confidence: 99%

Enhanced moth flame optimization based Supervision Kernel Entropy Component Analysis for high‐speed encrypted transmission model

Babu¹,

Kumar²

2021

Int J Communication

View full text Add to dashboard Cite

show abstract

“…However, the establishment of actual databases is a more serious challenge faced by deep learning algorithms. 22 Reference 23 uses the triangle shape and texture feature initialization area recognition based on the significant landmarks to normalize the shape and texture features from the triangle and texture area. And according to the shape and texture characteristics to determine the index as an important parameter for facial expression recognition.…”

Section: Related Researchmentioning

confidence: 99%

Multi‐angle face expression recognition based on generative adversarial networks

2021

Computational Intelligence

View full text Add to dashboard Cite

Because of the different features of the same facial expressions from different angles, most of the methods are only suitable for face images, and the accuracy of facial expression recognition is low. Therefore, a multi-angle facial expression recognition method based on generative adversarial networks (GAN) is proposed.Firstly, the depth regression network is used to detect the key points of the face image to achieve face alignment, so as to reduce the difficulty of feature extraction. Then, the image is input to GAN. The generator is composed of encoder and decoder, and a skip connection is designed.In the encoding phase, the generator can unlock the correlation between the facial expression image and the angle, and in the decoding stage, it can generate different angle facial expression images by adding other angle information. Finally, the multi-angle facial expression images are sent to the convolution neural network for classification and learning, in which the loss weight is adjusted dynamically to improve the recognition accuracy by introducing resistance loss, recognition loss, content loss, and center loss. The experimental results on Multi-pose illumination expression (PIE) and celebrities in frontal profile (CFP) datasets show that the performance of the proposed method is the best when the learning rate is 0.0002, and the recognition accuracy 20

show abstract

Facial expression recognition based on dual-feature fusion and improved random forest classifier

Cited by 25 publications

References 36 publications

Prediction and Knowledge Mining of Outdoor Atmospheric Corrosion Rates of Low Alloy Steels Based on the Random Forests Approach

Prediction and Knowledge Mining of Outdoor Atmospheric Corrosion Rates of Low Alloy Steels Based on the Random Forests Approach

Enhanced moth flame optimization based Supervision Kernel Entropy Component Analysis for high‐speed encrypted transmission model

Multi‐angle face expression recognition based on generative adversarial networks

Contact Info

Product

Resources

About