Artificial neural network for the recognition of human emotions under a backpropagation algorithm
Authors: Caicedo Alexander , Caicedo Anthony 
Published: December 2021

The era of the technological revolution increasingly encourages the development of technologies that facilitate in one way or another people's daily activities, thus generating a great advance in information processing. The purpose of this work is to implement a neural network that allows classifying the emotional states of a person based on the different human gestures. A database is used with information on students from the PUCE-E School of Computer Science and Engineering. Said information are images that express the gestures of the students and with which the comparative analysis with the input data is carried out. The environment in which this work converges proposes that the implementation of this project be carried out under the programming of a multilayer neuralnetwork. Multilayer feeding neural networks possess a number of properties that make them particularly suitable for complex pattern classification problems [8]. Back-Propagation [4], which is a backpropagation algorithm used in the Feedforward neural network, was taken into consideration to solve the classification of emotions.

Keywords: Image processing, neural networks, gestures, back-propagation, feedforward, classification, emotions.

References

[1]S. Gangwar, S. Shukla, D. Arora. “Human Emotion Recognition by Using Pattern Recognition Network”, Journal of Engineering Research and Applications, Vol. 3, Issue 5, pp.535-539, 2013.

[2]K. Rohit. “Back Propagation Neural Network based Emotion Recognition System”. International Journal of Engineering Trends and Technology (IJETT), Vol. 22, Nº 4, 2015.

[3]S. Eishu, K. Ranju, S. Malika, “Speech Emotion Recognition using BFO and BPNN”, International Journal of Advances in Science and Technology (IJAST), ISSN2348-5426, Vol. 2 Issue 3, 2014.

[4]A. Fiszelew, R. García-Martínez and T. de Buenos Aires. “Generación automática de redes neuronales con ajuste de parámetros basado en algoritmos genéticos”. Revista del Instituto Tecnológico de Buenos Aires, 26, 76-101, 2002.

[5]Y. LeCun, B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard, and L. Jackel. “Handwritten digit recognition with a back-propagation network”. In Advances in neural information processing systems. pp. 396-404, 1990.

[6]G. Bebis and M. Georgiopoulos. “Feed-forward neural networks”. IEEE Potentials, 13(4), 27-31, 1994.

[7]G. Huang, Q. Zhu and C. Siew. “Extreme learning machine: a new learning scheme of feedforward neural networks”. In Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference. Vol. 2, pp. 985-990. IEEE, 2004.

[8]D. Montana and L. Davis. “Training Feedforward Neural Networks Using Genetic Algorithms”. In IJCAI, Vol. 89, pp. 762-767, 1989.

[9]I. Sutskever, O. Vinyals and Q. Le. “Sequence to sequence learning with neural networks”. In Advances in neural information processing systems. pp. 3104-3112, 2014.

[10]J. Schmidhuber. “Deep learning in neural networks: An overview”. Neural networks, 61, 85-117, 2015.

[11]R. Santos, M. Ruppb, S. Bonzi and A. Filetia, “Comparación entre redes neuronales feedforward de múltiples capas y una red de función radial para detectar y localizar fugas en tuberías que transportan gas”. Chem. Ing.Trans , 32 (1375), e1380, 2013.