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Full Quaternion Matrix-Based Multiscale Principal Component Analysis Network for Facial Expression Recognition

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Pattern Recognition and Computer Vision (PRCV 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14429))

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Abstract

To acquire a more discriminative feature of facial expression, we propose a multi-scale principal component analysis network based on full quaternion matrix representation. Firstly, the structure feature and color components of facial image constitute a full quaternion matrix. Subsequently, two-staged quaternion principal component analysis is employed to learn convolutional filters. Among them, the feature maps of both stages are activated via nonlinear function. With binarization and coding, the local histograms are stacked together and fed to the classifier for expression matching. Experiments conducted on the RafD, MMI, NVIE, and KDEF datasets have demonstrated that the proposed method achieves higher recognition accuracy than several existing algorithms.

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References

  1. Sun, Z., Chiong, R., Hu, Z.: An extended dictionary representation approach with deep subspace learning for facial expression recognition. Neurocomputing 316, 1–9 (2018)

    Article  Google Scholar 

  2. Guo, Y., et al.: Facial expressions recognition with multi-region divided attention networks for smart education cloud applications. Neurocomputing 493, 119–128 (2022)

    Article  Google Scholar 

  3. Qian, Z., Mu, J., Tian, F.: Ventral-Dorsal Attention Capsule Network for facial expression recognition. Digit. Signal Process. 136, 103978 (2023)

    Article  Google Scholar 

  4. Yun, S.-S., Choi, J., Park, S.-K., Bong, G.-Y., Yoo, H.: Social skills training for children with autism spectrum disorder using a robotic behavioral intervention system. Autism Res. 10, 1306–1323 (2017)

    Article  Google Scholar 

  5. Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Transact. Neural Netw. Learn. Syst. 33(12), 6999–7019 (2022)

    Article  MathSciNet  Google Scholar 

  6. He, X., Zhang, W.: Emotion recognition by assisted learning with convolutional neural networks. Neurocomputing 291, 187–194 (2018)

    Article  Google Scholar 

  7. Sun, J., et al.: Cascade wavelet transform based convolutional neural networks with application to image classification. Neurocomputing 514, 285–295 (2022)

    Article  Google Scholar 

  8. Fan, T., et al.: A new deep convolutional neural network incorporating attentional mechanisms for ECG emotion recognition. Comput. Biol. Med. 159, 106938 (2023)

    Article  Google Scholar 

  9. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)

    Article  Google Scholar 

  10. Yu, J., et al.: Exploring large-scale unlabeled faces to enhance facial expression recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5802–5809. IEEE, Vancouver (2023)

    Google Scholar 

  11. Wang, L., Jia, G., Jiang, N., Wu, H., Yang, J.: Ease: robust facial expression recognition via emotion ambiguity-sensitive cooperative networks. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 218–227. ACM, Lisboa (2022)

    Google Scholar 

  12. Chan, T., Jia, K., Gao, S., Lu, J., Zeng, Z., Ma, Y.: PCANet: a simple deep learning baseline for image classification? IEEE Trans. Image Process. 24(12), 5017–5032 (2015)

    Article  MathSciNet  Google Scholar 

  13. Qaraei, M., Abbaasi, S., Ghiasi-Shirazi, K.: Randomized non-linear PCA networks. Inf. Sci. 545, 241–253 (2021)

    Article  MathSciNet  Google Scholar 

  14. Zhou, D., Feng, S.: M3SPCANet: a simple and effective ConvNets with unsupervised predefined filters for face recognition. Eng. Appl. Artif. Intell. 113, 104936 (2022)

    Article  Google Scholar 

  15. Shao, Z., Liu, X., Yao, Q., Qi, N., Shang, Y., Zhang, J.: Multiple-image encryption based on chaotic phase mask and equal modulus decomposition in quaternion gyrator domain. Signal Process-Image Commun. 80, 115662 (2020)

    Article  Google Scholar 

  16. Zeng, R., et al.: Color image classification via quaternion principal component analysis network. Neurocomputing 216, 416–428 (2016)

    Article  Google Scholar 

  17. Zou, C., Kou, K.I., Wang, Y., Tang, Y.Y.: Quaternion block sparse representation for signal recovery and classification. Signal Process. 179, 107849 (2021)

    Article  Google Scholar 

  18. Shi, J., Zheng, X., Wu, J., Gong, B., Zhang, Q., Ying, S.: Quaternion Grassmann average network for learning representation of histopathological image. Pattern Recogn. 89, 67–76 (2019)

    Article  Google Scholar 

  19. Liu, W., Kou, K.I., Miao, J., Cai, Z.: Quaternion scalar and vector norm decomposition: quaternion PCA for color face recognition. IEEE Trans. Image Process. 32, 446–457 (2022)

    Article  Google Scholar 

  20. Xu, Z., Shao, Z., Shang, Y., Li, B., Ding, H., Liu, T.: Fusing structure and color features for cancelable face recognition. Multimed. Tools Appl. 80, 14477–14494 (2021)

    Article  Google Scholar 

  21. Dubey, S.R., Singh, S.K., Chaudhuri, B.B.: Activation functions in deep learning: a comprehensive survey and benchmark. Neurocomputing 503, 92–108 (2022)

    Article  Google Scholar 

  22. Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)

    Google Scholar 

  23. Langner, O., Dotsch, R., Bijlstra, G., Wigboldus, D.H.J., Hawk, S.T., van Knippenberg, A.: Presentation and validation of the Radboud Faces Database. Cognit. Emot. 24(8), 1377–1388 (2010)

    Article  Google Scholar 

  24. Goeleven, E., De Raedt, R., Leyman, L., Verschuere, B.: The Karolinska directed emotional faces: a validation study. Cognit. Emot. 22(6), 1094–1118 (2008)

    Article  Google Scholar 

  25. Pantic, M., Valstar, M., Rademaker, R., Maat, L.: Web-based database for facial expression analysis. In: IEEE International Conference on Multimedia and Expo (ICME), pp. 317–321. IEEE, Amsterdam (2005)

    Google Scholar 

  26. Wang, S., et al.: A natural visible and infrared facial expression database for expression recognition and emotion inference. IEEE Trans. Multim. 12(7), 682–691 (2010)

    Article  Google Scholar 

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Acknowledgement

This work was supported by the National Natural Science Foundation of China (61876112, 61601311) and Science and Technology Planning Project of Jiaxing (2022AY10021).

Author information

Authors and Affiliations

  1. College of Information Engineering, Capital Normal University, Beijing, 100048, China

    Hangyu Li, Zuowei Zhang, Zhuhong Shao & Yuanyuan Shang

  2. College of Information Science and Engineering, Jiaxing University, Jiaxing, 314001, China

    Bin Chen

Authors
  1. Hangyu Li
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  2. Zuowei Zhang
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  3. Zhuhong Shao
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  4. Bin Chen
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  5. Yuanyuan Shang
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Corresponding author

Correspondence to Zhuhong Shao .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  2. Xiamen University, Xiamen, China

    Hanzi Wang

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Zhanyu Ma

  4. Sun Yat-sen University, Guangzhou, China

    Weishi Zheng

  5. Peking University, Beijing, China

    Hongbin Zha

  6. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  8. Xiamen University, Xiamen, China

    Rongrong Ji

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Li, H., Zhang, Z., Shao, Z., Chen, B., Shang, Y. (2024). Full Quaternion Matrix-Based Multiscale Principal Component Analysis Network for Facial Expression Recognition. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14429. Springer, Singapore. https://doi.org/10.1007/978-981-99-8469-5_10

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  • DOI: https://doi.org/10.1007/978-981-99-8469-5_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8468-8

  • Online ISBN: 978-981-99-8469-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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