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Erschienen in:
Active Shape Model vs. Deep Learning for Facial Emotion Recognition in Security Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Performance Analysis of Gesture Recognition Classifiers for Building a Human Robot Interface

verfasst von : Tiziana D’Orazio, Nicola Mosca, Roberto Marani, Grazia Cicirelli

Erschienen in: Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction

Verlag: Springer International Publishing

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Abstract

In this paper we present a natural human computer interface based on gesture recognition. The principal aim is to study how different personalized gestures, defined by users, can be represented in terms of features and can be modelled by classification approaches in order to obtain the best performances in gesture recognition. Ten different gestures involving the movement of the left arm are performed by different users. Different classification methodologies (SVM, HMM, NN, and DTW) are compared and their performances and limitations are discussed. An ensemble of classifiers is proposed to produce more favorable results compared to those of a single classifier system. The problems concerning different lengths of gesture executions, variability in their representations, generalization ability of the classifiers have been analyzed and a valuable insight in possible recommendation is provided.

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Vorheriges Kapitel Face Recognition in Home Security System Using Tensor Decomposition Based on Radix-(2 × 2) Hierarchical SVD
Nächstes Kapitel On Automatic Question Answering Using Efficient Primal-Dual Models
Literatur
1.
Murthy, G.R.S., Jadon, R.S.: A review of vision based hand gesture recognition. Int. J. Inform. Technol. Knowl. Manage. 2(2), 405–419 (2009)
2.
Hassan, M.H., Mishra, P.K.: Hand Gesture Modeling and ecognition using geometric features: a review. Canadian J. Image Process. Comput. Vis. 3(1), 12–26 (2012)
3.
Mitra, S., Acharya, T.: Gesture recognition: a survey. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 37(3), 311–324 (2007)CrossRef
4.
D’Orazio, T., Marani, R., Renó, V., Cicirelli, G.: Recent trends in gesture recognition: how depth data has improved classical approaches. Image Vis. Comput. 52, 56–72 (2016)CrossRef
5.
Aggarwal, J.K., Xia, L.: Human activity recognition from 3D data: a review. Pattern Recogn. Lett. 48, 70–80 (2014)CrossRef
6.
Chen, L., Wei, H., Ferryman, J.: A survey of human motion analysis using depth imagery. Pattern Recogn. Lett. 34(15), 1995–2006 (2013)CrossRef
7.
Cruz, L., Lucio, F., Velho, L.: Kinect and RGBD images: challenges and applications. In: XXV SIBGRAPI IEEE Confernce and Graphics, Patterns and Image Tutorials, pp. 36–49 (2012)
8.
Cheng, L., Sun, Q., Cong, Y., Zhao, S.: Design and implementation of human-robot interactive demonstration system based on kinect. In: 24th Chinese Control and Decision Conference (CCDC), pp. 971–975 (2012)
9.
Almetwally, I., Mallem, M.: Real-time tele-operation and tele-walking of humanoid Robot Nao using Kinect Depth Camera. In: 10th IEEE International Conference on Networking, Sensing and Control (ICNSC), pp. 463–466 (2013)
10.
Hachaj, T., Ogiela, M.R.: Rule-based approach to recognizing human body poses and gestures in real time. Multimedia Syst. 20, 81–99 (2013)CrossRef
11.
Jacob, M.G., Wachs, J.P.: Context-based hand gesture recognition for the operating room. Pattern Recogn. Lett. 36(15), 196–203 (2014)CrossRef
12.
Bodiroža, S., Doisy, G., Hafner, V.V.: Position-invariant, real-time gesture recognition based on Dynamic Time Warping. In: 8th ACM/IEEE International Conference on Human-robot Interaction, pp. 87–88 (2013)
13.
Lai, K., Konrad, J., Ishwar, P.: A gesture-driven computer interface using Kinect. In: IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) (2012)
14.
Oh, J., Kim, T., Hong, H.: Using binary decision tree and multiclass SVM for human gesture recognition. In: IEEE International Conference on Information Science and Applications (ICISA) (2013)
15.
Mangera, R.: Static gesture recognition using features extracted from skeletal data. In: 24th Annual Symposium of the Pattern Recognition Association of South Africa (2013)
16.
Gu, Y., Do, H., Ou, Y., Sheng, W.: Human gesture recognition through a Kinect sensor. In: IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1379–1384 (2012)
17.
Wang, Y., Yang, C., Wu, X., Xu, S., Li, H.: Kinect based dynamic hand gesture recognition algorithm research. In: 4th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), pp. 274–279 (2012)
18.
Song, Y., Gu, Y., Wang, P., Liu, Y., Li, A.: A Kinect based gesture recognition algorithm using GMM and HMM. In: 6th International Conference on Biomedical Engineering and Informatics (BMEI), pp. 750–754 (2013)
19.
Miranda, L., Vieira, T., Martinez, D., Lewiner, T., Vieira, A., Campos, M.: Online gesture recognition from pose kernel learning and decision forests. Pattern Recogn. Lett. 39, 65–73 (2014)CrossRef
20.
Bhattacharya, S., Czejdo, B., Perez, N.: Gesture classification with machine learning using Kinect sensor data. In: 3rd International Conference on Emerging Applications of Information Technology (EAIT), pp. 348–351 (2012)
21.
Althloothi, S., Mahoor, M.H., Zhang, X., Voyles, R.M.: Human activity recognition using multi-features and multiple kernel learning. Pattern Recogn. 47, 1800–1812 (2014)CrossRef
22.
Ibraheem, N.A., Khan, R.Z.: Vision based gesture recognition using neural networks approaches: a review. Int. J. Hum. Comput. Interac. (IJHCI) 3(1), 1–14 (2012)
23.
Cicirelli, G., Attolico, C., Guaragnella, C., D’Orazio, T.: A kinect-based gesture recognition approach for a natural human robot interface. Int. J. Adv. Robot. Syst. 12 (2015)
24.
Celebi, S., Aydin, A.S., Temiz, T.T., Arici, T.: Gesture recognition using skeleton data with weighted dynamic time warping. Comput. Vis. Theory Appl. 1, 620–625 (2013)
25.
Ding, I.J., Chang, C.W.: An eigenspace-based method with a user adaptation scheme for human gesture recognition by using Kinect 3D data. Appl. Math. Model. 39(19), 5769–5777 (2015)CrossRef
26.
D’Orazio, T., Attolico, C., Cicirelli, G., Guaragnella, C.: A neural network approach for human gesture recognition with a kinect sensor. In: International Conference on Pattern Recognition Applications and Methods (ICPRAM), pp. 741–746 (2014)
27.
Yule, G.: On the association of attributes in statistics. Phil. Trans. 194, 257–319 (1990)CrossRefMATH
28.
Polikar, R.: Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6(3), 21–45 (2006)CrossRef
Metadaten
Titel
Performance Analysis of Gesture Recognition Classifiers for Building a Human Robot Interface
verfasst von
Tiziana D’Orazio
Nicola Mosca
Roberto Marani
Grazia Cicirelli
Copyright-Jahr
2017
Buch
Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction

Print ISBN: 978-3-319-59258-9

Electronic ISBN: 978-3-319-59259-6

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-59259-6_6