E-MobileNeXt: face expression recognition model based on improved MobileNeXt

REVINA I M, EMMANUEL W R S. A survey on human face expression recognition techniques[J]. Journal of King Saud University-computer and information sciences, 2021, 33(6): 619–628.

Article  Google Scholar 

LI S, DENG W. Deep facial expression recognition: a survey[J]. IEEE transactions on affective computing, 2020, 13(3): 1195–1215.

Article  Google Scholar 

YU Z, ZHANG C. Image based static facial expression recognition with multiple deep network learning[C]//Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, November 9–13, 2015, Washington, USA. New York: Association for Computing Machinery, 2015: 435–442.

Google Scholar 

JIANG S, XU X, LIU F, et al. CS-GResNet: a simple and highly efficient network for facial expression recognition[C]//2022 IEEE International Conference on Acoustics, May 22–27, 2022, Singapore. New York: IEEE, 2022: 2599–2603.

Google Scholar 

BARROS P, CHURAMANI N, SCIUTTI A. The facechannel: a light-weight deep neural network for facial expression recognition[C]//2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), November 16–20, 2020, Buenos Aires, Argentina. New York: IEEE, 2020: 652–656.

Google Scholar 

RODOLFO F P, MITRE H H. ResMoNet: a residual mobile-based network for facial emotion recognition in resource-limited systems[EB/OL]. (2020-05-15) [2023-04-10]. https://arxiv.org/abs/2005.07649.

ZHOU D, HOU Q, CHEN Y, et al. Rethinking bottleneck structure for efficient mobile network design[C]//Computer Vision-ECCV 2020: 16th European Conference, August 23–28, 2020, Glasgow, UK. Berlin, Heidelberg: Springer-Verlag, 2020: 680–697.

Google Scholar 

DING X, ZHANG X, MA N, et al. Repvgg: making vgg-style convnets great again[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 20–25, 2021, Nashville, TN, USA. New York: IEEE, 2021: 13733–13742.

Google Scholar 

HAN K, WANG Y, TIAN Q, et al. Ghostnet: more features from cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 13–19, 2020, Seattle, WA, USA. New York: IEEE, 2020: 1580–1589.

Google Scholar 

SINHA D, EL-SHARKAWY M. Thin mobilenet: an enhanced mobilenet architecture[C]//2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), October 10–12, 2019, New York, USA. New York: IEEE, 2019: 0280–0285.

Google Scholar 

LI X, HU X, YANG J. Spatial group-wise enhance: improving semantic feature learning in convolutional networks[EB/OL]. (2019-05-23) [2023-04-10]. https://arxiv.org/abs/1905.09646v1.

LI S, DENG W, DU J P. Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, July 21–26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 2852–2861.

Google Scholar 

LUCEY P, COHN J F, KANADE T, et al. The extended cohn-kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression[C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, June 13–18, 2010, San Francisco, CA, USA. New York: IEEE, 2010: 94–101.

Google Scholar 

NIGAM S, SINGH R, MISRA A K. Efficient facial expression recognition using histogram of oriented gradients in wavelet domain[J]. Multimedia tools and applications, 2018, 77: 28725–28747.

Article  Google Scholar 

LIU P, HAN S, MENG Z, et al. Facial expression recognition via a boosted deep belief network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 23–28, 2014, Columbus, OH, USA. New York: IEEE, 2014: 1805–1812.

Google Scholar 

WANG Z, ZENG F, LIU S, et al. OAENet: oriented attention ensemble for accurate facial expression recognition[J]. Pattern recognition, 2021, 112: 107694.

Article  Google Scholar 

HUA C H, HUYNH T T, SEO H, et al. Convolutional network with densely backward attention for facial expression recognition[C]//2020 14th International Conference on Ubiquitous Information Management and Communication (IMCOM), January 3–5, 2020, Taichung, China. New York: IEEE, 2020: 1–6.

Google Scholar 

GAN C, XIAO J, WANG Z, et al. Facial expression recognition using densely connected convolutional neural network and hierarchical spatial attention[J]. Image and vision computing, 2022, 117: 104342.

Article  Google Scholar 

GHOSH S, DHALL A, SEBE N. Automatic group affect analysis in images via visual attribute and feature networks[C]//2018 25th IEEE International Conference on Image Processing (ICIP), October 7–10, 2018, Athens, Greece. New York: IEEE, 2018: 1967–1971.

Google Scholar 

LI Y, ZENG J, SHAN S, et al. Occlusion aware facial expression recognition using CNN with attention mechanism[J]. IEEE transactions on image processing, 2018, 28(5): 2439–2450.

Article  ADS  MathSciNet  Google Scholar 

ZENG J, SHAN S, CHEN X. Facial expression recognition with inconsistently annotated datasets[C]//Proceedings of the European Conference on Computer Vision (ECCV), September 8–14, 2018, Munich, Germany. Berlin, Heidelberg: Springer-Verlag, 2018: 227–243.

Google Scholar 

留言 (0)

沒有登入
gif