Abstract
Emotions are the determinant of what makes us human. They influence each and every aspect of our everyday life, including concentration, perception, memory, decision making and contact with society. Human beings are exceptionally skilled and competent at reading and interpreting faces through evolution. In addition, facial recognition is so essential to human survival that an area called the Fusiform Face Area (FFA) had been developed for it by the human brain. In this paper, the key proposal is implementing multilevel Haar wavelet-based technique also known as Haar cascade face identification algorithm, which pulls out several expression attributes from major face regions at different levels. The translation of the structure and the arrangement of 43 facial muscles into emotions can be a difficult task to execute. So, we will Train FER2013 Dataset in Google collab for 25 epochs, which will churn out training accuracy to more than 90%. Many biometric technologies are available in our day-to-day activities to identify people, such as eye recognition, fingerprint recognition, face recognition. Face is a prime portion of the human being and needs detection for various applications such as forensic investigation, protection. The accuracy of the given technique is trialed on popular FER2013 facial expression dataset, and it achieves 91.85% accuracy for the given dataset.
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References
Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., Taylor, J.G.: Emotion recognition in human-computer interaction. IEEE Signal Process. Mag. 18(1), 32–80 (2001)
Content derived from: https://www.researchgate.net/publication/264151703_Region-Based_Facial_Expression_
Viola, P., Jones, M.: Rapid object detection using boosted cascade of simple features. In: IEEE Conference on Computer Vision and Pattern Recognition (2001)
Dachapally, P.R.: Facial emotion detection using convolutional neural networks and representational autoencoder units. School of Informatics and Computing Indiana University, 5 Jun 2017
Baboota R., Kaur H.: Predictive analysis and modelling football results using machine learning approach for English Premier League. Int. J. Forecast. 35 (2):741-755 (2019)
Rzayeva, Z., Alasgarov, E.: Facial emotion recognition using convolutional neural networks. In: 2019 IEEE 13th International Conference on Application of Information and Communication Technologies (AICT)
Sang, D.V., Van Dat, N., Thuan, D.P.: Facial expression recognition using deep convolutional neural networks. In: 2017 9th International Conference on Knowledge and Systems Engineering (KSE), Hue, pp. 130–135 (2017)
Kharkovyna, O.: An intro to deep learning for facial recognition, 26 June 2019. www.medium.com
Sharma, H.: Facial Expressions Recognition, 21 April 2018. www.medium.com
Alto, V.: Face Recognition with OpenCV: Haar Cascade, 16 Jul 2019. www.medium.com
Wilson, P.I., Fernandez, J.D.: Facial Feature Detection Using Haar Classifier. J. Comput. Sci. (2006)
Inspiration derived from: https://medium.com/analytics-vidhya/haar-cascade-face-identification-aa4b8bc794
Minaee, S., Abdolrashidi, A.: Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network. Expedia Group University of California, Riverside, USA
Sharma, N.: How to do Facial Emotion Recognition Using A CNN? 24 Dec 2018. www.medium.com
Michael Revina, I., Sam Emmanuel, W.R.: A Survey on Human Face Expression Recognition Techniques. Reg No. 12417, N.M. Christian College, Marthandam Affiliated to Manonmaniam Sunadaranar University
Blog on “Algorithmia” quoted. Introduction to Facial Emotion Recognition, 28 Feb 2018
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Alankar, B., Ammar, M.S., Kaur, H. (2021). Facial Emotion Detection Using Deep Learning and Haar Cascade Face Identification Algorithm. In: Das, S., Mohanty, M.N. (eds) Advances in Intelligent Computing and Communication. Lecture Notes in Networks and Systems, vol 202. Springer, Singapore. https://doi.org/10.1007/978-981-16-0695-3_17
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DOI: https://doi.org/10.1007/978-981-16-0695-3_17
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