Abstract
Develo** systems and devices that can recognize, interpret, and process human emotions are an interdisciplinary field involving computer science, psychology, and cognitive science. A system has been developed in order to formally categorize the emotions depending on facial expressions. The feature selection is done based on facial action coding system which is basically a contraction or relaxation of one or more face muscles. Our goal is to categorize the facial expression using image into six basic emotional states: Happy, Sad, Anger, Fear, Disgust, and Surprise. Extraction of facial features from eye, mouth, eyebrow, and nose is performed by employing an iterative search algorithm, on the edge information of the localized face region in binary scale. Finally, emotion class assignment is done by applying the extracted blocks as inputs to a feed-forward neural network trained by back-propagation algorithm.
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References
Ekman P, Rosenberg EL (eds) (2005) What the face reveals: basic and applied studies of spontaneous expression using the facial action coding system (FACS), 2nd edn. Oxford University Press, Oxford, pp 271–286
Raheja JL, Kumar U (2002) Human facial-expression detection from detected in captured image using back-propagation neural network, digital systems group, central electronics engineering research institute (CEERI)/council of scientific and industrial research (CSIR), Rajasthan
Peter C, Herbon A (2006) Emotion representation and physiology assignments in digital systems. Interact Comput (Elsevier) 18:139–170
Taylor JG, Fragopanagos NF (2005) The interaction of attention and emotion. Neural Netw (Elsevier) 18:353–369
Gunes H, Piccardi M (2005) Fusing face and body gesture for machine recognition of emotions, 2005 IEEE international workshop on robots and human interactive communication, pp 94–99
Wilson PI, Dr Fernandez J (2006) Facial feature detection using haar classifiers, Texas A&M University, JCSC 21(4)
Shivakumar G, Vijaya PA, Anand RS (2007) Artificial neural network based cumulative scoring pattern method for ECG analysis. IEEE international conference on advances in computer vision and information technology, Aurangabad, pp 451–457
Shivakumar G, Vijaya PA (2011) Analysis of human emotions using galvanic skin response and finger tip temperature. Int J Synth Emotions (US) 2(1):15–25
Shivakumar G, Vijaya PA (2009) Face recognition system using back propagation artificial neural network. Int J Comput Sci Inf Technol 1(1):68–77
Ross A, Jain A (2003) Information fusion in biometrics. Pattern Recogn Lett (Elsevier) 24:2115–2125
Tian YL, Kanade T, Cohn JF (2001) Recognizing action units for facial expression analysis. IEEE Trans Pattern Anal Mach Intell 23(2):97–113
Shivakumar G, Vijaya PA (2012) Emotion recognition using finger tip temperature: first step towards an automatic system. Int J Comput Electr Eng (Singap) 4(3):252–255
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© 2013 Springer India
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Shivakumar, G., Vijaya, P.A. (2013). An Improved Artificial Neural Network Based Emotion Classification System for Expressive Facial Images. In: Chakravarthi, V., Shirur, Y., Prasad, R. (eds) Proceedings of International Conference on VLSI, Communication, Advanced Devices, Signals & Systems and Networking (VCASAN-2013). Lecture Notes in Electrical Engineering, vol 258. Springer, India. https://doi.org/10.1007/978-81-322-1524-0_31
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DOI: https://doi.org/10.1007/978-81-322-1524-0_31
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