Deep neural network-based emotion recognition using facial landmark features and particle swarm optimization

References

• Vaijayanthi S, Arunnehru J. Human emotion recognition from body posture with machine learning techniques. Commun Comput Inf Sci. 2022;1613:231–242. doi:10.1007/978-3-031-12638-3_20
   Google Scholar
• Vaijayanthi S, Arunnehru J. Synthesis approach for emotion recognition from cepstral and pitch coefficients using machine learning. Singapore: Springer; 2021; p. 515–528.
   Google Scholar
• Kuusikko S, Haapsamo H, Jansson-Verkasalo E, et al. Emotion recognition in children and adolescents with autism spectrum disorders. J Autism Dev Disord 2009;39(6):938–945. doi:10.1007/s10803-009-0700-0
   PubMed Web of Science ®Google Scholar
• Hung JC, Lin KC, Lai NX. Recognizing learning emotion based on convolutional neural networks and transfer learning. Appl Soft Comput J. Nov 2019;84:105724. doi:10.1016/j.asoc.2019.105724
   Web of Science ®Google Scholar
• Arunnehru J, Geetha MK. Motion intensity code for action recognition in video using PCA and SVM; 2013. doi:10.1007/978-3-319-03844-5_8
   Google Scholar
• Sekar V, Jawaharlalnehru A. Semantic-based visual emotion recognition in videos-a transfer learning approach. Int J Electr Comput Eng. 2022;12(4):3674–3683. doi:10.11591/ijece.v12i4.pp3674-3683
   Google Scholar
• Clark EA, Kessinger J, Duncan SE., et al. The facial action coding system for characterization of human affective response to consumer product-based stimuli: a systematic review. Front Psychol 2020;11:1–21. doi:10.3389/fpsyg.2020.00920
   PubMed Web of Science ®Google Scholar
• Arunnehru J, Kumar A, Verma JP. Early prediction of brain tumor classification using convolution neural networks. Commun Comput Inf Sci. 2020;1192:16–25. doi:10.1007/978-981-15-3666-3_2
   Google Scholar
• Gunes H, Piccardi M. Bi-modal emotion recognition from expressive face and body gestures. J Netw Comput Appl Nov. 2007;30(4):1334–1345. doi:10.1016/j.jnca.2006.09.007
   Web of Science ®Google Scholar
• Fan X, Tjahjadi T. A spatial-temporal framework based on histogram of gradients and optical flow for facial expression recognition in video sequences. Pattern Recognit 2015;48(11):3407–3416. doi:10.1016/j.patcog.2015.04.025
   Web of Science ®Google Scholar
• Ghimire D, Lee J. Geometric feature-based facial expression recognition in image sequences using multi-class AdaBoost and support vector machines. Sensors (Switzerland). 2013;13(6):7714–7734. doi:10.3390/s130607714
   PubMed Web of Science ®Google Scholar
• Aifanti N, Delopoulos A. Linear subspaces for facial expression recognition. Signal Process Image Commun. 2014;29(1):177–188. doi:10.1016/j.image.2013.10.004
   Web of Science ®Google Scholar
• Chickerur S, Reddy T, Shabalina O. Parallel scale invariant feature transform based approach for facial expression recognition. Commun. Comput. Inf. Sci. 2015;535:621–636.
   Google Scholar
• Fathallah A, Abdi L, Douik A. Facial expression recognition via deep learning. In: Proc. IEEE/ACS Int. Conf. Comput. Syst. Appl. AICCSA, vol. 2017; Oct 2018. p. 745–750. doi:10.1109/AICCSA.2017.124
   Google Scholar
• Taghi Zadeh MM, Imani M, Majidi B. Fast facial emotion recognition using convolutional neural networks and gabor filters. In: 2019 IEEE 5th Conf. Knowl. Based Eng. Innov. KBEI 2019; 2019. p. 577–581. doi:10.1109/KBEI.2019.8734943
   Google Scholar
• Verma M, Vipparthi SK, Singh G. Hinet: hybrid inherited feature learning network for facial expression recognition. IEEE Lett. Comput. Soc. 2019;2(4):36–39. doi:10.1109/LOCS.2019.2927959
   Google Scholar
• Ravi R, Yadhukrishna SV, Prithviraj R. A face expression recognition using CNN LBP. In: Proc. 4th Int. Conf. Comput. Methodol. Commun. ICCMC 2020; 2020. p. 684–689. doi:10.1109/ICCMC48092.2020.ICCMC-000127
   Google Scholar
• Chowdary MK, Nguyen TN, Hemanth DJ. Deep learning-based facial emotion recognition for human–computer interaction applications. Neural Comput Appl 2023;35(32):23311–23328. doi:10.1007/s00521-021-06012-8
   Web of Science ®Google Scholar
• Siam AI, Soliman NF, Algarni AD, et al. Deploying machine learning techniques for human emotion detection. Comput Intell Neurosci. 2022;2022:8032673. doi:10.1155/2022/8032673
   PubMedGoogle Scholar
• Talaat FM, Ali ZH, Mostafa RR, et al. Real-time facial emotion recognition model based on kernel autoencoder and convolutional neural network for autism children. Soft Comput 2024; 1–14. doi:10.1007/s00500-023-09477-y
   Web of Science ®Google Scholar
• Aifanti N, Papachristou C, Delopoulos A. The mug facial expression database. In: 11th Int. Work. Image Anal. Multimed. Interact. Serv. WIAMIS 10, January 2016; 2010.
   Google Scholar
• Dael N, Mortillaro M, Scherer KR. Emotion expression in body action and posture. Emotion. 2012;12(5):1085–1101. doi:10.1037/a0025737
   PubMed Web of Science ®Google Scholar
• Vaijayanthi S, Arunnehru J. Facial Expression Recognition Using Hyper-Complex Wavelet Scattering and Machine Learning Techniques. In: Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering: ICACIE 2021. Singapore: Springer Nature Singapore; 2002. p. 411–421.
   Google Scholar
• Mohd Yamin MN, Aziz KA, Siang TG, et al. Particle swarm optimisation for emotion recognition systems: a decade review of the literature. Appl Sci. 2023;13(12), 7054. doi:10.3390/app13127054
   Google Scholar
• Zhang Y, Yan L. Face recognition algorithm based on particle swarm optimization and image feature compensation. SoftwareX. 2023;22:101305. doi:10.1016/j.softx.2023.101305
   Web of Science ®Google Scholar
• Donuk K, Ari A, Özdemir MF, et al. Deep feature selection for facial emotion recognition based on BPSO and SVM. Politek Derg. 2023;26(1):131–142. doi:10.2339/politeknik.992720
   Google Scholar
• Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84–90. doi:10.1145/3065386
   Web of Science ®Google Scholar
• Vaijayanthi S, Arunnehru J. Dense SIFT-based facial expression recognition using machine learning techniques. In: Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering; 2023. p. 301–310.
   Google Scholar