nach oben

Erschienen in:

2020 | OriginalPaper | Buchkapitel

Human Emotion Recognition Using Body Expressive Feature

verfasst von : R. Santhoshkumar, Dr. M. Kalaiselvi Geetha

Erschienen in: Microservices in Big Data Analytics

Verlag: Springer Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Recognition of emotions from human plays a vital role in our day-to-day life and is essential for social communication. In many application of human–computer interaction using nonverbal communication like facial expression, body movements, eye movements and gestures are used. Among these methods, body movement method is widely used because it predicts the emotions of human. In this paper, body expressive features (angle, distance, velocity and acceleration) are proposed to recognize the emotion from human body movements. The GEMEP corpus (straight view) videos are used for this experiment. The 12-dimensional features were extracted from the head point, left-hand point and right-hand point of body movements of the human present in the frame. The features are given to the random forest (RF) classifier to predict the human emotions. The performance measure can be calculated using qualitative and quantitative analyses.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Implementation of Block-Based Symmetric Algorithms for Real Image Encryption

Nächstes Kapitel Self-energizing Wireless Sensor Network

Glowinski, D., Mortillaro, M., Scherer, K., Dael, N., Camurri, G.V.A.: Towards a minimal representation of affective gestures. Affect. Comput. Intell. Interaction. IEEE 498–504 (2015)

Castellano, G., Villalba, S.D., Camurri, A.: Recognizing human emotions from body movement and gesture dynamics. Int. Conf. Affect. Comput. Intell. Interact., Springer 71–82 (2007)

Santhoshkumar, R., Geetha, M.K., Arunnehru, J.: SVM—KNN based emotion recognition of human in video using HOG feature and KLT tracking algorithm. Int. J. Pure Appl. Math. 117(15), 621–634 (2017)

Shafir, T., Tsachor, R.P., Welch, K.B.: Emotion regulation through movement: unique sets of movement characteristics are associated with and enhance basic emotions. Front. Psychol. 6, 1–15 (2016)CrossRef

Saha, S., Datta, S., Konar, A., Janarthanan, R.: A study on emotion recognition from body gestures using kinect sensor. Commun. Signal Processing. IEEE 056–060 (2014)

Arunnehru, J., Kalaiselvi Geetha, M.: Motion intensity code for action recognition in video using PCA and SVM. Min. Intell. Knowl. Explor. 8284, 70–81 (2013)

Arunnehru, J., Kalaiselvi Geetha, M.: Behavior recognition in surveillance video using temporal features. In: 4th ICCCNT, Thiruchengode, India (2013)

J. Arunnehru., M. Kalaiselvi Geetha., Automatic Activity Recognition for Video Surveillance. International Journal of Computer Application. Vol.75, 9, 1–6 (2013)CrossRef

J. Arunnehru., M. Kalaiselvi Geetha., Automatic human emotion recognition in surveillance video. Intelligent Techniques in Signal Processing for Multimedia Security, pp. 321–342. Springer (2017)

10.

Varghese, A.A., Cherian, J.P., Kizhakkethottam, J.J.: Overview on emotion recognition system. In: International Conference on Soft-Computing and Network Security (2015)

11.

Piana, S., Stagliano, A., Odone, F., Verri, A., Camurri, A.: Real-time automatic emotion recognition from body gestures. Human-Computer Interaction. Computer Vision and Pattern Recognition (2014)

12.

Karg, M., Samadani, A.A., Gorbet, R., Kühnlenz, K., Hoey, J., Kulić, D.: Body movements for affective expression: a survey of automatic recognition and generation. IEEE Trans. Affect. Comput. 4, 4 (2013)CrossRef

13.

Glowinski, D., Dael, N., Camurri, A., Volpe, G., Mortillaro, M., Scherer, K.: Toward a minimal representation of affective gestures. IEEE Trans. Affect. Comput. 2(2) (2011)CrossRef

14.

Wang, W., Enescu, V., Sahli, H.: Adaptive real-time emotion recognition from body movements. ACM Trans. Interact. Intell. Syst. 5(4) (2015)CrossRef

15.

Fourati, N., Pelachaud, C.: Multi-level classification of emotional body expression. IEEE (2015)

16.

Prinzie, A., Van den Poel, D., Random Forests for multiclass classification: random multinomial logit. Expert Syst. Appl. 34(3), 1721–1732CrossRef

17.

Acharjya, D.P., Geetha, M.K. Sanyal, S.: Internet of Things: Novel Advances and Envisioned Applications. Springer International Publishing, USA: Springer. ISBN 978-3-319-53470-1, ISSN 2197-6511, pp. 1–399. https://doi.org/10.1007/978-3-319-53472-5 (2017)

18.

Kalaiselvi Geetha, M., Palanivel, S.: Video classification and shot detection for video retrieval applications. Int. J. Comput. Intell. Syst. 2(1), 39–50 (2009)CrossRef

19.

Chitra, M., Geetha, M.K., Menaka, L.: Occlusion and abondoned object detection for Surveillance applications. Int. J. Comput. Appl. Technol. Res. 2(6), 708–713 (2013)CrossRef

20.

Rajesh, P., Geetha, M.K., Ramu, R.: Traffic density estimation, vehicle classification and stopped vehicle detection for traffic surveillance system using predefined traffic videos. Int. J. Elixir Comput. Sci. Eng. 56, Number A, 13671–13676 (2013)

21.

Punitha, A., Kalaiselvi Geetha, M., Sivaprakash, A.: Driver fatigue monitoring system based on eye state analysis. In: International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014], IEEE, pp. 1405–1408 (2014)

22.

Bänziger, T., Mortillaro, M., Scherer, K.R.: Introducing the geneva multimodal expression corpus for experimental research on emotion perception. Emotion 12(5), 1161–1179 (2012)CrossRef

23.

Bänziger, T., Scherer, K.R.: Introducing the Geneva Multimodal Emotion Portrayal (GEMEP) corpus. In: Blueprint for Affective Computing: A Sourcebook Oxford. England: Oxford University Press. 271–294 (2010)

Titel: Human Emotion Recognition Using Body Expressive Feature
verfasst von: R. Santhoshkumar
Dr. M. Kalaiselvi Geetha
Verlag: Springer Singapore
Buch: Microservices in Big Data Analytics
Print ISBN: 978-981-15-0127-2

Electronic ISBN: 978-981-15-0128-9

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-981-15-0128-9_13

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"