Abstract
The growing era of facial recognition has opened a large area of computational study. Facial emotion recognition has always been a challenging task in the fields of deep learning. In this work, we have proposed a better approach to not only study human facial emotion, but also predict one’s emotion by collecting the same personal data. One part of the article represents the usage of CNN for detecting facial emotions in which it takes in real-time video frames and predicts the probabilities of the seven basic emotion states. Output data of the CNN model serves as the input for the time-series analysis model, and the task of predicting one’s future emotions has been accomplished. The two-step hierarchical structure helps in studying human behaviour to predict future outcomes. Finally, the model can be used for continuous monitoring and predicting a person’s behaviour providing constant emotional parameters. This work will be used in many interrogatory procedures or as a preventive measure by collections of the convict’s facial data.
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References
Adhikari R, Agrawal RK (2013) An introductory study on time series modeling and forecasting
Alam MGR, Abedin SF, Moon S II, Talukder A, Hong CS (2019) Healthcare IoT-based affective state mining using a deep convolutional neural network. IEEE Access 7:1–15. https://doi.org/10.1109/ACCESS.2019.2919995
Arriaga O, Valdenegro-Toro M, Plöger PG (2019) Real-time convolutional neural networks for emotion and gender classification. In: ESANN 2019—Proceedings, 27th European symposium on artificial neural networks, computational intelligence and machine learning, pp 221–226
Available online. https://www.researchgate.net/publication/3940582-Rapid-Object-Detection-using-Boosted-Cascade-of-Simple-Features. Accessed: 03-Sept-2020
Behera B, Kumar N, Mahato MR, Prasad BK, Semwal VB (2020a) Weather forecasting and monitoring using machine learning. In: National conference on electronics, communication and computation—NCECC 2020. MANTECH Publications, Jamshedpur, pp 1–6
Behera B, Kumar N, Mahato MR, Kumar A (2020b) COVID-19 detection using advanced CNN and X-rays. In: Arpaci I et al (eds) Emerging technologies during the era of COVID-19 pandemic. Springer Nature, Berlin, pp 1–11
Choi DY, Song BC (2020) Facial micro-expression recognition using two-dimensional landmark feature maps. IEEE Access 8:121549–121563. https://doi.org/10.1109/ACCESS.2020.3006958
Chollet F (2017) Xception: deep learning with depth wise separable convolutions, pp 1–8. http://arxiv.org/abs/161002357v3. arXiv: 161002357v3. https://doi.org/10.1109/CVPR.2017.195
Clark EA, Kessinger J, Duncan SE et al (2020) The facial action coding system for characterisation of human affective response to consumer product-based stimuli: a systematic review. Front Psychol 11:1–21. https://doi.org/10.3389/fpsyg.2020.00920
Cuimei L, Zhiliang Q, Nan J, Jianhua W (2017) Human face detection algorithm via Haar cascade classifier combined with three additional classifiers. In: IEEE 13th International conference on electronic measurement & instruments. IEEE, pp 483–487
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE Conference on computer vision and pattern recognition, pp 770–778
Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) MobileNets: efficient convolutional neural networks for mobile vision applications, pp 1–9. http://arxiv.org/abs/170404861v1. arXiv:170404861v1
Kaehler A, Bradski G (2016) Learning OpenCV 3: computer vision in C++ with the OpenCV library, 1st edn. O’Reilly, Sebastopol
Mehrabian A (2017) Nonverbal communication. Taylor & Francis Group, New York, USA
Polusmak E (2017) Time series analysis in Python: predicting the future with Facebook Prophet. In: mlcourse.ai. https://mlcourse.ai/articles/topic9-part2-prophet/. Accessed: 03-Sept-2020
Saha S (2018) A comprehensive guide to convolutional neural networks—the ELI5 way. In: Towards Data Science. https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53. Accessed: 03-Sept-2020
Semwal VB, Singha J, Sharma PK, Chauhan A, Behera B (2017) An optimized feature selection technique based on incremental feature analysis for bio-metric gait data classification. Multimed Tools Appl 76:24457–24475. https://doi.org/10.1007/s11042-016-4110-y
Shaver P, Schwartz J, Kirson D, O’Connor C (1987) Emotion knowledge: further exploration of a prototype approach. J Pers Soc Psychol 52:1061–1086
Sun X, Zheng S, Fu H (2020) ROI-attention vectorized CNN model for static facial expression recognition. IEEE Access 8:7183–7194. https://doi.org/10.1109/ACCESS.2020.2964298
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2015) Rethinking the inception architecture for computer vision, pp 1–8. arXiv: 151200567v3. https://doi.org/10.1109/CVPR.2016.308
Taylor SJ, Letham B (2018) Forecasting at scale. In: American Statistician. Available online. https://facebook.github.io/prophet/. Accessed: 03-Sept-2020
Tutorials O-P (2020) Face detection using Haar cascades. In: OpenCV. https://opencv-python-tutroals.readthedocs.io/en/latest/py-tutorials/py-objdetect/py-face-detection/py-face-detection.html. Accessed: 03-Sept-2020
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Behera, B., Prakash, A., Gupta, U., Semwal, V.B., Chauhan, A. (2021). Statistical Prediction of Facial Emotions Using Mini Xception CNN and Time Series Analysis. In: Verma, G.K., Soni, B., Bourennane, S., Ramos, A.C.B. (eds) Data Science. Transactions on Computer Systems and Networks. Springer, Singapore. https://doi.org/10.1007/978-981-16-1681-5_25
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