Abstract
Facial expressions are utilized often in the day to day communication and are considered significant as they can mirror the internal emotional states of a person. Automatic Facial Expression Recognition (FER) systems aim at classifying the facial images into various expressions. To do this task accurately, better feature descriptors are to be developed to effectively capture the facial information. In this regard, novel local texture based feature extraction technique, Petersen Graph based Binary Pattern (PGBP), inspired by the Generalized Petersen Graph has been proposed. PGBP extracts three feature values in a 5\(\,\times \,\)5 overlapping neighborhood. The experiments have been performed on MUG, TFEID and KDEF datasets with respect to six expressions in person independent setup. The experimental results demonstrated that the proposed method outperformed the existing methods in terms of recognition accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Shan, C., Gong, S., McOwan, P.W.: Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis. Comput. 27(6), 803–816 (2009)
Iqbal, M.T.B., et al.: Facial expression recognition with neighborhood-aware edge directional pattern (NEDP). IEEE Trans. Affect. Comput. 11(1), 125–137 (2018)
Ryu, B., Rivera, A.R., Kim, J., Chae, O.: Local directional ternary pattern for facial expression recognition. IEEE Trans. Image Process. 26(12), 6006–6018 (2017)
Kung, H.-W., Yi-Han, T., Hsu, C.-T.: Dual subspace nonnegative graph embedding for identity-independent expression recognition. IEEE Trans. Inf. Forensics Secur. 10(3), 626–639 (2015)
Lee, S.H., Plataniotis, K.N., Ro, Y.M.: Intra-class variation reduction using training expression images for sparse representation based facial expression recognition. IEEE Trans. Affect. Comput. 5(3), 340–351 (2014)
Xie, S., Haifeng, H., Yongbo, W.: Deep multi-path convolutional neural network joint with salient region attention for facial expression recognition. Pattern Recogn. 92, 177–191 (2019)
Revina, I.M., Emmanuel, W.R.S.: MDTP: a novel multi-directional triangles pattern for face expression recognition. Multimedia Tools Appl. 78(18), 26223–26238 (2019)
Sen, D., Datta, S., Balasubramanian, R.: Facial emotion classification using concatenated geometric and textural features. Multimedia Tools Appl. 78(8), 10287–10323 (2019)
Verma, M., Vipparthi, S.K., Singh, G.: HiNet: hybrid inherited feature learning network for facial expression recognition. IEEE Lett. Comput. Soc. 2(4), 36–39 (2019)
Sun, Z., Hu, Z.P., Wang, M., Zhao, S.H.: Individual-free representation-based classification for facial expression recognition. SIViP 11(4), 597–604 (2017)
Ali, A.M., Zhuang, H., Ibrahim, A.K.: An approach for facial expression classification. Int. J. Biom. 9(2), 96–112 (2017)
Qin, Y.-L., Xia, B., Zhou, S.: Canonical double covers of generalized Petersen graphs, and double generalized Petersen graphs. J. Graph Theory 97(1), 70–81 (2021)
Mandal, M., et al.: Regional adaptive affinitive patterns (RADAP) with logical operators for facial expression recognition. IET Image Process. 13(5), 850–861 (2019)
Kola, D.G.R., Samayamantula, S.K.: A novel approach for facial expression recognition using local binary pattern with adaptive window. Multimedia Tools Appl. 80(2), 2243–2262 (2021)
Aifanti, N., Papachristou, C., Delopoulos, A.: The MUG facial expression database. In: 11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 2010, pp. 1–4. IEEE (2010)
Chen, L.-F., Yen, Y.-S.: Taiwanese facial expression image database. Brain Mapping Laboratory, Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan (2007)
Goeleven, E., De Raedt, R., Leyman, L., Verschuere, B.: The Karolinska directed emotional faces: a validation study. Cogn. Emot. 22(6), 1094–1118 (2008)
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vision 57(2), 137–154 (2004)
Shanthi, P., Nickolas, S.: An efficient automatic facial expression recognition using local neighborhood feature fusion. Multimedia Tools Appl. 80(7), 10187–10212 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 Springer Nature Switzerland AG
About this paper
Cite this paper
Kartheek, M.N., Prasad, M.V.N.K., Bhukya, R. (2024). Petersen Graph Based Binary Pattern for Person Independent Facial Expression Recognition. In: Ghosh, A., King, I., Bhattacharyya, M., Sankar Ray, S., K. Pal, S. (eds) Pattern Recognition and Machine Intelligence. PReMI 2021. Lecture Notes in Computer Science, vol 13102. Springer, Cham. https://doi.org/10.1007/978-3-031-12700-7_44
Download citation
DOI: https://doi.org/10.1007/978-3-031-12700-7_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-12699-4
Online ISBN: 978-3-031-12700-7
eBook Packages: Computer ScienceComputer Science (R0)
