Abstract
One of the most versatile ways in which individuals express their state of mind is through facial expressions. The advancement of deep learning-based technologies helped us to detect human emotion from images that can be used for understanding human feelings as well. The image can be static or can be captured through a web camera in real time. The precise analysis of human facial expressions is necessary for a better understanding of human behaviour. With the recent progress in deep learning, Convolution Neural Network (CNN) with its enhanced complex architecture is capable of emotion detection in a much better and more efficient way. In this paper, we experiment and demonstrate how to build a CNN predictor model using TensorFlow that can predict the emotion from images of human facial expressions with satisfactory accuracy. Additionally, we also develop an application that asks for image input from the user and predicts the emotion from the given input image. Through this experiment, we are successful in demonstrating how CNN is an appropriate model for this task. Our work is beneficial in many applications such as lie detectors and student assessments to detect facial expressions very accurately.
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References
Snigdha S et al (2022) Astronomical big data processing using machine learning: a comprehensive review. Experiment Astron 1–43. https://doi.org/10.1007/s10686-021-09827-4
Sandeep VY, Sen S, Santosh K (2021) Analysing and processing of astronomical images using deep learning techniques. In: 2021 IEEE international conference on electronics, computing and communication technologies (CONNECT). IEEE. https://doi.org/10.1109/CONECCT52877.2021.9622583
Sen S et al (2021) Implementation of neural network regression model for faster redshift analysis on cloud-based spark platform. In: International conference on industrial, engineering and other applications of applied intelligent systems. Springer, Cham. https://doi.org/10.1007/978-3-030-79463-7_50
Monisha R, Sen S, Davangeri RU, Sri Lakshmi KS, Dey S (2022) An approach toward design and implementation of distributed framework for astronomical big data processing. In: Intelligent systems. Springer, Singapore, pp 267–275. https://doi.org/10.1007/978-981-19-0901-6_26
https://www.simplilearn.com/tutorials/deep-learning-tutorial/deep-learning-algorithm
Sen S et al (2021) Analysis, visualization and prediction of COVID-19 pandemic spread using machine learning. In: Innovations in computer science and engineering. Springer, Singapore, pp 597–603
Sen S, Singh KP, Chakraborty P (2023) Dealing with imbalanced regression problem for large dataset using scalable Artificial Neural Network. New Astron 99:101959
Sen, S, Amrita I (2022) A transfer learning based approach for lung inflammation detection. In: Advanced techniques for IoT applications: proceedings of EAIT 2020. Springer, Singapore
Mayank K, Sen S, Chakraborty P (2022) Implementation of cascade learning using apache spark. In: 2022 IEEE international conference on electronics, computing and communication technologies (CONECCT). IEEE
Khasnis NS, Sen S, Khasnis SS (2021) A machine learning approach for sentiment analysis to nurture mental health amidst COVID-19. In: Proceedings of the international conference on data science, machine learning and artificial intelligence
Pankaj, Sen S, Chakraborty P (2022) A novel classification-based approach for quicker prediction of redshift using apache spark. In: 2022 International conference on data science, agents & artificial intelligence (ICDSAAI). Chennai, India, pp 1–6. https://doi.org/10.1109/ICDSAAI55433.2022.10028971
Corneanu CA, Simón MO, Cohn JF, Guerrero SE (2016) Survey on RGB, 3D, thermal, and multimodal approaches for facial expression recognition: History, trends, and affect-related applications. IEEE Trans Pattern Anal Mach Intell 38:1548–1568. https://doi.org/10.1109/TPAMI.2016.2515606
Matsugu M, Mori K, Mitari Y, Kaneda Y (2003) Subject independent facial expression recognition with robust face detection using a convolutional neural network. Neural Netw 16:555–559. https://doi.org/10.1016/S0893-6080(03)00115-1
Fasel B (2002) Robust face analysis using convolutional neural networks. In: Proceedings of the 16th international conference on pattern recognition; Quebec City, QC, Canada, pp 40–43
Anil J, Suresh LP (2016) Literature survey on face and face expression recognition. In: Proceedings of the 2016 international conference on circuit, power and computing technologies (ICCPCT); Nagercoil, India, pp 1–6
Mohammed AA, Minhas R, Wu QJ, Sid-Ahmed MA (2011) Human face recognition based on multidimensional PCA and extreme learning machine. Patt Recognit 44:2588–2597. https://doi.org/10.1016/j.patcog.2011.03.013
Rivera AR, Castillo JR, Chae OO (2013) Local directional number pattern for face analysis: face and expression recognition. IEEE Trans Image Process 22:1740–1752. https://doi.org/10.1109/TIP.2012.2235848
Shan C, Gong S, McOwan PW (2009) Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis Comput 27:803–816. https://doi.org/10.1016/j.imavis.2008.08.005
Yu Z, Zhang C (2015) Image-based static facial expression recognition with multiple deep network learning. In: Proceedings of the 2015 ACM on international conference on multimodal interaction. Seattle, WA, USA. New York, NY, USA: ACM, pp 435–442
Kahou SE, Pal C, Bouthillier X, Froumenty P, Gülçehre Ç, Memisevic R, Vincent P, Courville A, Bengio Y, Ferrari RC et al (2013) Combining modality specific deep neural networks for emotion recognition in the video. In: Proceedings of the 15th ACM on international conference on multimodal interaction. Sydney, Australia, New York, NY, USA: ACM, pp 543–550
Ebrahimi Kahou S, Michalski V, Konda K, Memisevic R, Pal C (2015) ICMI ‘15, Proceedings of the 2015 ACM on International Conference on Multimodal Interaction. ACM; New York, NY, USA. Recurrent Neural Networks for Emotion Recognition in Video, pp 467–474
Goodfellow IJ, Erhan D, Carrier PL, Courville A, Mirza M, Hamner B, Cukierski W, Tang Y, Thaler D, Lee DH et al (2013) Challenges in representation learning: a report on three machine learning contests. In: International conference on neural information processing. Springer, pp 117–124
https://www.analyticsvidhya.com/blog/2021/11/facial-emotion-detection-using-cnn/
Kumar S, Yadav D, Gupta H et al (2022) Towards smart surveillance as an aftereffect of COVID-19 outbreak for recognition of face masked individuals using YOLOv3 algorithm. Multimed Tools Appl. https://doi.org/10.1007/s11042-021-11560-1
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Sahana, D., Varsha, K.S., Sen, S., Priyanka, R. (2023). A CNN-Based Approach for Facial Emotion Detection. In: Kumar, R., Verma, A.K., Sharma, T.K., Verma, O.P., Sharma, S. (eds) Soft Computing: Theories and Applications. Lecture Notes in Networks and Systems, vol 627. Springer, Singapore. https://doi.org/10.1007/978-981-19-9858-4_1
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DOI: https://doi.org/10.1007/978-981-19-9858-4_1
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