Abstract
Emotion is an integral part of everyone’s life. With developments in new products in everyday life, it is needed to identify what people think of that in an instant procedure, so better solutions could be provided. For the identification of emotions, it is required to observe the speech and facial gestures of the user. Everywhere in the world is equipped with cameras from laptops to high buildings. With the abundant data of images, it is necessary to build a model that can detect emotion using images or frames without trading for either speed or accuracy. With advancements in computer vision and deep learning, an emotion of the user identity is detected with high accuracy; since computer vision is an automated process, the results are much faster than the regular methods of manual surveys. In this project, the SAVEE database is used, which comprises audio and amp; visual features of seven unique types of emotions; and these emotions are identified by using CNN-based systems exploiting facial gestures of actors. Important features from the faces of the actors in the database are extracted and trained using existing deep learning methods namely 3D convnets. Testing has provided the maximum accuracy of 95.83% for emotion recognition from facial gestures.
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
References
Zhang, K., Huang, Y., Du, Y., Wang, L.: Facial expression recognition based on deep evolutional spatial-temporal networks IEEE Trans. Image Process 26(9), 4193–4203 (2017)
Jung, H., Lee, S., Yim, J., Park, S., Kim, J.: Joint fine-tuning in deep neural networks for facial expression recognition. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 2983–2991. IEEE (2015)
Li, S., Deng, W.: Deep facial expression recognition: a survey. In: IEEE Transactions on Affective Computing, (2020) https://doi.org/10.1109/TAFFC.2020.2981446
Kishore, K.K., Satish, P.K.: Emotion recognition in speech using MFCC and wavelet features. In: 2013 3rd IEEE International Advance Computing Conference (IACC) Ghaziabad, pp. 842–847 (2013). https://doi.org/10.1109/IAdCC.2013.6514336
Sinith, M.S., Aswathi, E., Deepa, T.M., Shameema, C.P., Rajan, S.: Emotion recognition from audio signals using support vector machine. In: 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS) Trivandrum, pp. 139–144 (2015). ase10.1109/RAICS.2015.7488403
Fan, Y., et al.: Video-based emotion recognition using CNN-RNN and C3D hybrid networks. In: Proceedings of the 18th ACM International Conference on Multimodal Interaction (2016)
Sharma, S., Shanmugasundaram, K., Ramasamy, S.K.: FAREC—CNN based efficient face recognition technique using Dlib. In: 2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), Ramanathapuram, pp. 192–195 (2016) https://doi.org/10.1109/ICACCCT.2016.7831628.
Noroozi, F., Kaminska, D., Sapinski, T., Anbarjafari, G.: Supervised vocal-based emotion recognition using multiclass support vector machine, random forests, and adaboost. J. Audio Eng. Soc. 657/8, (2017). https://doi.org/10.17743/jaes.2017.0022
Revathi, A., Nagakrishnan, R., Vashista, D.V., Teja, K.S.S., Sasikaladevi, N.: Emotion recognition from speech using perceptual features and convolutional neural networks (2020). https://doi.org/10.1007/978-981-15-3992-3_29
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Teja, K.S.S., Reddy, T.V., Sashank, M., Revathi, A. (2022). 3D CNN Based Emotion Recognition Using Facial Gestures. In: Bhateja, V., Tang, J., Satapathy, S.C., Peer, P., Das, R. (eds) Evolution in Computational Intelligence. Smart Innovation, Systems and Technologies, vol 267. Springer, Singapore. https://doi.org/10.1007/978-981-16-6616-2_30
Download citation
DOI: https://doi.org/10.1007/978-981-16-6616-2_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6615-5
Online ISBN: 978-981-16-6616-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)