Abstract
Every person expresses himself or herself with different facial expressions. Nowadays, recognition of facial expression is a popular topic for researchers due to the practical function in a wide variety of areas. The learning of human facial expressions is a tough and tricky domain in pattern research society. Each individual facial expression is produced by non-rigid object contortions, and these contortions are man-dependent. The main motto of this paper was to invent and apply a structure for the automatic recognition of human facial expression.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Y. Tian, T. Kanade J.F. Cohn. Recognizing action units for facial expression analysis. IEEE Trans. Pattern Anal. Mach. Intell., 23(2), (2001)
M.F. Valstar, M. Pantic. Fully automatic recognition of the temporal phases of facial actions. IEEE Trans. Syst., Man Cybern.-Part B: Cybern. 42(1), (2012)
L. Florea, R. Boia. Eyebrow localization for expression analysis. In: Intelligent Computer Communication and Processing (ICCP), 2011 IEEE International Conference Publications, 2011
M. Suwa, N. Sugie, K. Fujimori. A preliminary note on pattern recognition of human emotional expression (1978)
P. Ekman, W. Friesen, The facial action coding system: a technique for the measurement of facial movement (1978)
A.K. Jain, S.Z. Li, Handbook of Face Recognition (Springer-Verlag, New York Inc, Se caucus, NJ, USA, 2005)
M. Pantic, I. Patras, Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences. IEEE Trans. Syst., Man, Cybern., Part B 36(2), 433–449 (2006)
K.-K. Sung, T. Poggio. Example-based learning for view-based human face detection (1998)
H. A. Rowley, S. Baluja, T. Kanade, Neural network-based face detection (1998)
E. Osuna, R. Freund, F. Girosi. Training support vector machines: an application to face detection (1997)
H. Schneiderman, T. Kanade. A statistical method for 3d object detection applied to faces and cars (2000)
P. Viola, M. Jones. Rapid object detection using a boosted cascade of simple features (2001)
F. Crow, Summed-area tables for texture mapping (1984)
P.Y. Simard, L. Bottou, P. Haffner, Y.L. Cun, Boxlets: a fast convolution algorithm for signal processing and neural networks (1998)
P. Viola, M. Jones. Robust real-time object detection (2001)
S.Z. Li, L. Zhu, Z.Q. Zhang, A. Blake, H. Zhang, H. Shum, Statistical learning of multi-view face detection (2002)
S.Z. Li, Z. Zhang. Floatboost learning and statistical face detection (2004)
S. Li, Z.Q. Zhang, H.-Y. Shum, H. Zhang. Floatboost learning for classification (2002)
K. Fukui, O. Yamaguchi, Facial feature point extraction method based on a combination of shape extraction and pattern matching. Syst. Comput. Jpn. 29(6), 49–58 (1998)
G. Donato, M.S. Bartlett, J.C. Hager, P. Ekman, T.J. Sejnowski, Classifying facial actions. Int. J. Pattern Anal. Mach. Intell. 21(10), 974–989 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Banerjee, T., De, S., Das, S., Sarkar, S., Swarnakar, S. (2020). Automatic Facial Expression Recognition Using Geometrical Features. In: Mandal, J., Bhattacharya, D. (eds) Emerging Technology in Modelling and Graphics. Advances in Intelligent Systems and Computing, vol 937. Springer, Singapore. https://doi.org/10.1007/978-981-13-7403-6_45
Download citation
DOI: https://doi.org/10.1007/978-981-13-7403-6_45
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-7402-9
Online ISBN: 978-981-13-7403-6
eBook Packages: EngineeringEngineering (R0)