Expression recognition based on residual rectifier enhanced convolution neural network

Chen, Bin; Zhu, Jin-ning; Dong, Yi-zhou

doi:10.37188/yjyxs20203512.1299

Cited by 1 publication

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The outstanding performance of sparse coding algorithm in image denoising, clustering, deblur and classification has attracted more researchers [28]. Our research group and related research teams have made remarkable achievements in expression recognition with sparse coding as a classification method [29]. The sparse representation uses a linear dictionary sequence of the high-level form as the input signal, and its dictionary learning ability is designed to infer the [30].…”

Section: Deep Sparse Convolutional Neural Networkmentioning

confidence: 99%

“…Our research group and related research teams have made remarkable achievements in expression recognition with sparse coding as a classification method [29]. The sparse representation uses a linear dictionary sequence of the high-level form as the input signal, and its dictionary learning ability is designed to infer the [30]. The performance of a deep network increases with the increase of the depth and width of the network, but the disadvantage is that a large number of parameters need to be adapted.…”

Section: Deep Sparse Convolutional Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

Age Operator Deep Sparse Fusion Extension of the Expression Recognition

Chen

Shen

Fan

2023

Preprint

View full text Add to dashboard Cite

To solve the problem of trait differences in expression characteristics among different age groups, shortage of research on cross-age facial expression analysis, and the lack of datasets across ages, put forward the model in age factor deep sparse fusion extension of the expression recognition. The model utilizes a linear dictionary sequence of a high-level form as an input signal for a sparse representation, the input objects are constructed in a linear combination, and select the optimal solution in all solutions with the sparse level as an indicator, further through the convolutional, pooling and full connection processing of convolutional neural networks, the problems of high similarity, insufficient quantity and uneven distribution of data sets are solved through the fusion extension strategy, on this basis, the age operator is combined as feature elements and expression features are extracted and used as the basis for classification decision. Extension by deep sparse fusion, additional factors were extracted using anthropometric models, internal angle calculation and skin wrinkle detection of age operator as expression features, the comparison of results of multiple dataset experiments demonstrate the effectiveness and stability across datasets of the algorithm, the advantages of this algorithm are also demonstrated by the horizontal alignment of the representative cross-age expression classification algorithm. This method is effective for the accuracy and robustness of expression expressions across ages, it has the research value and the reference significance.

show abstract

Section: Deep Sparse Convolutional Neural Networkmentioning

confidence: 99%

Section: Deep Sparse Convolutional Neural Networkmentioning

confidence: 99%