Abstract
Gestures have been called the leaky source of emotional information. Also gestures are easy to retrieve from a distance by ordinary cameras. Thus as many would agree gestures become an important clue to the emotional state of a person. In this paper we have worked on recognizing emotions of a person by analyzing only gestural information. Subjects are initially trained to perform emotionally expressive gestures by a professional actor. The same actor trained the system to recognize the emotional context of gestures. Finally the gestural performances of the subjects are evaluated by the system to identify the class of emotion indicated. Our system yields an accuracy of 94.4% with a training set of only one gesture per emotion. Apart from this our system is also computationally efficient. Our work analyses emotions from only gestures, which is a significant step towards reducing the cost efficiency of emotion recognition. It may be noted here that this system may also be used for the purpose of general gesture recognition. We have proposed new features and a new classifying approach using fuzzy sets. We have achieved state of art accuracy with minimal complexity as each motion trajectory along each axis generates only 4 displacement features. Each axis generates a trajectory and only 6 joint trajectories among all joint trajectories are compared. The 6 motion trajectories are selected based on maximum motion, as maximum moving regions give more information on gestures. The experiments have been performed on data obtained from Microsoft Kinect sensors. Training and Testing were subject gender independent.
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References
Ju, Z., Liu, H.: A unified fuzzy framework for human-hand motion recognition. IEEE Transactions on Fuzzy Systems 19(5), 901–913 (2011)
Liao, J., Bi, Y., Nugent, C.: Using the Dempster–Shafer theory of evidence with a revised lattice structure for activity recognition. IEEE Transactions on Information Technology in Biomedicine 15(1), 74–82 (2011)
Schwier, J.M., Brooks, R.R., Griffin, C.: Methods to window data to differentiate between Markov models. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 41(3), 650–663 (2011)
Lin, C.T., Yeh, C.M., Liang, S.F., Chung, J.F., Kumar, N.: Support-vector-based fuzzy neural network for pattern classification. IEEE Transactions on Fuzzy Systems 14(1), 31–41 (2006)
Sotelo, M.P., Desseree, E., Moreau, J.M., Shariat, B., Beuve, M.: 3-D Model-Based Multiple-Object Video Tracking for Treatment Room Supervision. IEEE Transactions on Biomedical Engineering 59(2), 562–570 (2012)
Fang, G., Gao, W., Zhao, D.: Large vocabulary sign language recognition based on fuzzy decision trees. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 34(3), 305–314 (2004)
Zhang, X., Chen, X., Li, Y., Lantz, V., Wang, K., Yang, J.: A framework for hand gesture recognition based on accelerometer and EMG sensors. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 41(6), 1064–1076 (2011)
Zadeh, L.A.: Fuzzy logic = computing with words. IEEE Transactions on Fuzzy Systems 4(2), 103–111 (1996)
Sarkar, N., Smith, C.A.: A novel interface system for seamlessly integrating human-robot cooperative activities in space. Technical Report, NASA Institute for Advanced Concepts (2002)
Jiang, M., Wang, Z.: A method for stress detection based on FCM algorithm. In: 2nd International Congress on Image and Signal Processing, CISP 2009, pp. 1–5. IEEE (2009)
Guney, K., Sarikaya, N.: Comparison of Mamdani and Sugeno fuzzy inference system models for resonant frequency calculation of rectangular microstrip antennas. Progress In Electromagnetics Research B 12, 81–104 (2009)
Bailador, G., Guadarrama, S.: Robust gesture recognition using a prediction-error-classification approach. In: IEEE International Fuzzy Systems Conference, 2007 (FUZZ-IEEE 2007), pp. 1–7. IEEE (2007)
de Santos Sierra, A., Sánchez Ávila, C., Casanova, J.G., del Pozo, G.B.: A stress-detection system based on physiological signals and fuzzy logic. IEEE Transactions on Industrial Electronics, 58(10), 4857-4865 (2011)
Várkonyi-Kóczy, A.R., Tusor, B.: Human–computer interaction for smart environment applications using fuzzy hand posture and gesture models. IEEE Transactions on Instrumentation and Measurement 60(5), 1505–1514 (2011)
Álvarez, M., Galán, R., Matía, F., Rodríguez-Losada, D., Jiménez, A.: An emotional model for a guide robot. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 40(5), 982–992 (2010)
Liu, H.Y., Wang, W.J., Wang, R.J., Tung, C.W., Wang, P.J., Chang, I.P.: Image Recognition and Force Measurement Application in the Humanoid Robot Imitation. IEEE Transactions on Instrumentation and Measurement 61(1), 149–161 (2012)
Yang, F., Li, Y.: Set-membership fuzzy filtering for nonlinear discrete-time systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 40(1), 116–124 (2010)
Kadkhodaie-Ilkhchi, A., Monteiro, S.T., Ramos, F., Hatherly, P.: Rock recognition from mwd data: A comparative study of boosting, neural networks, and fuzzy logic. IEEE Geoscience and Remote Sensing Letters 7(4), 680–684 (2010)
Castellano, G., Villalba, S.D., Camurri, A.: Recognising human emotions from body movement and gesture dynamics. In: Paiva, A.C.R., Prada, R., Picard, R.W. (eds.) ACII 2007. LNCS, vol. 4738, pp. 71–82. Springer, Heidelberg (2007)
Castellano, G., Mortillaro, M., Camurri, A., Volpe, G., Scherer, K.: Automated analysis of body movement in emotionally expressive piano performances. Music Perception: An Interdisciplinary Journal 26(2), 103–119 (2008)
Gunes, H., Shan, C., Chen, S., Tian, Y.: Bodily Expression for Automatic Affect Recognition. Advances in Emotion Recognition. Wiley-Blackwell
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Kar, R., Chakraborty, A., Konar, A., Janarthanan, R. (2013). Emotion Recognition System by Gesture Analysis Using Fuzzy Sets. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8298. Springer, Cham. https://doi.org/10.1007/978-3-319-03756-1_32
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DOI: https://doi.org/10.1007/978-3-319-03756-1_32
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