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Facial Expression Recognition Using OpenCV and CNN Model

L. Siva Prasad, E.Giribabu Goud, V. Harshitha, C.Pavan Sai, P.Tharun K

Abstract


Thise paper presents a real-time facial emotion recognition system using convolutional neural networks (CNNs) and OpenCV. The system processes video frames in real -time to detect faces and recognize emotions from the facial expressions. The CNN model is trained on a large dataset of facial images and emotions, and the results demonstrate accurate and fast emotion recognition performance. The integration of OpenCV with the CNN model enables real-time processing of video frames, making the system suitable for various practical applications. One use of machine learning is the identification of facial expressions of emotion. AfterThat were extractioned from an image based on the features, it assigns a face emotion image to one of the facial emotion classes. Among the classification techniques, CNNconvolutional neural network (cnn) also pulls patterns from a picture. In this study we used the CNN model to recognize the facial expressions. To increase the precision of facial emotion detection, the wavelet transform is then used. There are seven different face emotions represented in the facial emotion image dataset that was gathered from Kaggle. The accuracy of the experimental facial emotion recognition utilizsing the CNN and wavelet transform increases.


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References


Koujan MR, Alharbawee L, Giannakakis G, Pugeault N, Roussos A. Real-time facial expression recognition “in the wild” by disentangling 3D expression from identity. In: 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), Buenos Aires, Argentina. 2020, November 16–20. pp. 24–31.

Haghpanah MA, Saeedizade E, Masouleh MT, Kalhor A. Real-time facial expression recognition using facial landmarks and neural networks. In: 2022 IEEE International Conference on Machine Vision and Image Processing (MVIP), Ahvaz Iran. 2022, February 23–24. pp. 1–7.

Bisogni C, Castiglione A, Hossain S, Narducci F, Umer S. Impact of deep learning approaches on facial expression recognition in healthcare industries. IEEE TransIndus Inform. 2022; 18 (8):

–5627.

Modi S, Bohara MH. Facial emotion recognition using convolution neural network. In: 2021 5th IEEE International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India. 2021, May 6–8. pp. 1339–1344.

Pham L, Vu TH, Tran TA. Facial expression recognition using residual masking network. In: 2020 25th IEEE International Conference on Pattern Recognition (ICPR), Milan, Italy. 2021. January 10–15. pp. 4513–4519.

Zhang K, Li Y, Wang J, Cambria E, Li X. Real-time video emotion recognition based on reinforcement learning and domain knowledge. IEEE Trans Circuits Syst Video Technol. 2021; 32 (3): 1034–1047.

Alexandre GR, Soares JM, Pereira GA. Systematic review of 3D facial expression recognition methods. Pattern Recogn. 2020; 100: 107108.

Dufourq E. A survey on factors affecting facial expression recognition based on convolutional neural networks. In: 2020 Conference of the South African Institute of Computer Scientists and Information Technologists, Cape Town, South Africa. 2020, September 14–16. pp. 168–179.

Dino H, Abdulrazzaq MB, Zeebaree SR, Sallow AB, Zebari RR, Shukur HM, Haji LM. Facial expression recognition based on hybrid feature extraction techniques with different classifiers. TEST Eng Manage. 2020; 83: 22319–22329.

Naga P, Marri SD, Borreo R. Facial emotion recognition methods, datasets and technologies: a literature survey. Mater Today Proc. 2023; 80 (Part 3): 2824–2828.

Singh S, Nasoz F. Facial expression recognition with convolutional neural networks. In 2020 10th IEEE Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA. 2020, January 6–8. pp. 0324–0328).

Kodhai E, Pooveswari A, Sharmila P, Ramiya N. Literature review on emotion recognition system. In: 2020 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), Puducherry, India. 2020, July 3. pp. 1–4.

Dixit AN, Kasbe T. A survey on facial expression recognition using machine learning techniques. In: 2nd IEEE International Conference on Data, Engineering and Applications (IDEA), Bhopal, India. 2020, February 28–29. pp. 1–6.

Barros P, Churamani N, Sciutti A. The facechannel: a fast and furious deep neural network for facial expression recognition. SN Computer Sci. 2020; 1: Article 321.

Satapathy A, Livingston LJ. A lite convolutional neural network built on permuted Xceptio-inception and Xceptio-reduction modules for texture based facial liveness recognition. Multimedia Tools Appl. 2021; 80: 10441–10472.

Liu S, Chen Z, Wang F, Zhao Z. Multi-angle face recognition based on convolutional neural network. J North China Univ Technol (Nat Sci Ed). 2019; 41 (4): 103–108.


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