nach oben

Pattern Recognition and Image Analysis

Erschienen in:

01.10.2020 | APPLIED PROBLEMS

Multi-View Facial Expression Recognition with Multi-View Facial Expression Light Weight Network

verfasst von: Shao Jie, Qian Yongsheng

Erschienen in: Pattern Recognition and Image Analysis | Ausgabe 4/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Facial expression recognition for frontal faces has become a well-established research area in the last two decades. However, non-frontal facial expression recognition hasn’t been paid much attention until recently. In this paper, we propose an MVFE-LightNet (Multi-View Facial Expression Light Weight Network) for multi-view facial expression recognition. To this end, we first applied MTCNN for facial detection and alignment and then did preprocessing like normalization and data augmentation. Finally, we put the images into MVFE-LightNet to extract sub-space features of facial expressions with various poses. A depthwise separable residual convolution module architecture was designed to reduce the parameters of the model and lessen the chance of overfitting. Experiments were implemented on Radboud Faces Database and BU-3DFE dataset. We demonstrated that our method could effectively improve the recognition accuracy, and achieved the accuracy of 95.6% and 88.7% respectively for the Radboud and BU-3DFE.

Vorheriger Artikel A Metric Learning Approach for Offline Writer Independent Signature Verification

Nächster Artikel Automatic Skin Lesion Segmentation—A Novel Approach of Lesion Filling through Pixel Path

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

H. Jung, S. Lee, J. Yim, S. Park, and J. Kim, “Joint fine-tuning in deep neural networks for facial expression recognition”, in Proceedings of the IEEE International Conference on Computer Vision (Santiago, 2015), pp. 2983–2991.

N. Hesse, T. Gehrig, H. Gao, and H. K. Ekenel, “Multi-view facial expression recognition using local appearance features,” in Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012) (Tsukuba, 2012), pp. 3533–3536.

O. Rudovic, I. Patras, and M. Pantic, “Coupled Gaussian process regression for pose-invariant facial expression recognition,” in Computer Vision–ECCV 2010 (Crete, 2010), pp. 350–363.

S. Moore and R. Bowden, “Local binary patterns for multiview facial expression recognition,” Comput. Vision Image Understanding 115 (4), 541–558 (2011).CrossRef

C. Shan, S. Gong, and P. W. McOwan, “Facial expression recognition based on local binary patterns: A comprehensive study”, Image Vision Comput. 27 (6), 803–816 (2009).CrossRef

G. Fanelli, A. Yao, P.-L. Noel, J. Gall, and L. Van Gool, “Hough forest-based facial expression recognition from video sequences,” in European Conference on Computer Vision (Crete, 2010), pp. 195–206.

Y. Ding, Q. Zhao, B. Li, and X. Yuan, “Facial expression recognition from image sequence based on LBP and Taylor expansion,” IEEE Access 5, 19409–19419 (2017).CrossRef

B. Ryu, A. R. Rivera, J. Kim, and O. Chae, “Local directional ternary pattern for facial expression recognition,” IEEE Trans. Image Process. 26 (12), 6006–6018 (2017).MathSciNetCrossRef

O. Rudovic, I. Patras, and M. Pantic, “Regression-based multi-view facial expression recognition,” in 2010 20th International Conference on Pattern Recognition (Istanbul, 2010), pp. 4121–4124.

10.

Q. Mao, Q. Rao, Y. Yu, and M. Dong, “Hierarchical Bayesian theme models for multi-pose facial expression recognition,” IEEE Trans. Multimedia 19 (4), 861–873 (2017).CrossRef

11.

T. Zhang, W. Zheng, Z. Cui, Y. Zong, J. Yan, and K. Yan, “A deep neural network-driven feature learning method for multi-view facial expression recognition,” IEEE Trans. Multimedia 18 (12), 2528–2536 (2016).CrossRef

12.

A. Mollahosseini, D. Chan, and M. H. Mahoor, “Going deeper in facial expression recognition using deep neural networks,” in IEEE Winter Conference on Applications of Computer Vision (2016), pp. 1–10.

13.

D. V. Sang, L. T. B. Cuong, and P. T. Ha, “Discriminative deep feature learning for facial emotion recognition,” in 2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR) (Ho Chi Minh City, 2018), pp. 1–6.

14.

K. Shan, J. Guo, W. You, D. Lu, and R. Bie, “Automatic facial expression recognition based on a deep convolutional-neural-network structure,” in 2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA) (London 2017), pp. 123–128.

15.

W. Ge and Y. Yu, “Borrowing treasures from the wealthy: Deep transfer learning through selective joint fine-tuning,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (Honolulu, HI, 2017).

16.

Y. Zhou and B. E. Shi, “Action unit selective feature maps in deep networks for facial expression recognition,” in 2017 International Joint Conference on Neural Networks (IJCNN) (Anchorage, AK, 2017), pp. 2031–2038.

17.

J. Luttrell et al., “A deep transfer learning approach to fine-tuning facial recognition models,” in 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA) (Wuhan, 2018), pp. 2671–2676.

18.

J. Xiang and G. Zhu, “Joint face detection and facial expression recognition with MTCNN,” in 2017 4th International Conference on Information Science and Control Engineering (Changsha, 2017), pp. 424–427.

19.

A. Halevy, P. Norvig, and F. Pereira, “The unreasonable effectiveness of data,” Intell. Syst. 24 (2), 8–12 (2009).CrossRef

20.

X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in International Conference on Artificial Intelligence and Statistics (Ft. Lauderdale, 2012), pp. 315–323.

21.

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International Conference on Machine Learning (Lille, 2015), pp. 448–456.

22.

O. Langner, R. Dotsch, G. Bijlstra, D. Wigboldus, S. Hawk, and A. Knippenberg, “Presentation and validation of the Radboud Faces Database,” Cognit. Emotion 24 (8), 1377–1388 (2010).CrossRef

23.

L. Yin, X. Wei, Y. Sun, J. Wang, and M. J. Rosato, “A 3D facial expression database for facial behavior research,” in Automatic face and Gesture Recognition 2006. FGR 2006. 7th International Conference on (Southampton, 2006), pp. 211–216.

24.

F. Chollet, Keras (2015). https://github.com/fchollet/keras.

25.

W. Zheng, H. Tang, Z. Lin, et al., “A novel approach to expression recognition from non-frontal face images,” in IEEE International Conference on Computer Vision (Kyoto, 2011), pp. 1901–1908.

26.

Wenming Zheng, “Multi-view facial expression recognition based on group sparse,” IEEE Trans. Affective Comput. 5 (1), 71–85 (2014).CrossRef

Titel: Multi-View Facial Expression Recognition with Multi-View Facial Expression Light Weight Network
verfasst von: Shao Jie
Qian Yongsheng
Publikationsdatum: 01.10.2020
Verlag: Pleiades Publishing
Erschienen in: Pattern Recognition and Image Analysis / Ausgabe 4/2020
Print ISSN: 1054-6618
Elektronische ISSN: 1555-6212
DOI: https://doi.org/10.1134/S1054661820040197

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 4/2020

Descriptive Image Analysis: Part IV. Information Structure for Generating Descriptive Algorithmic Schemes for Image Recognition

Computational Approaches to Aesthetic Quality Assessment of Digital Photographs: State of the Art and Future Research Directives

Time-Aware Social Hierarchical Poisson Factorization for Personalized Recommendation

Image Captioning using Reinforcement Learning with BLUDEr Optimization

Robust and Fast Hypothesis Verification in 3D Object Recognition

Wrapper Filter Approach for Accelerometer-Based Human Activity Recognition