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Facial Expression Recognition based on Feature Enhancement and Improved Alexnet


Affiliations
1 Department of Electronics and Communication Engineering, Gujarat Technological University, India, India
2 Department of Electronics and Communication Engineering, Government Engineering College, Bhavnagar, India, India
 

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For interpersonal relations among humans, facial expressions are extremely important. Due to the complications in collecting required features from the frequently changing surroundings, uneven reflection from light sources, and many other aspects, facial expression recognition will encounter significant problems. A novel facial image recognition approach is proposed in this paper. Initially, a face image enhancement framework is created that is capable of enhancing the features of a face in a complicated context for this strategy. The improved Alexnet neural network is then created, which is based on the Alexnet architecture. Multi-scale convolution process is utilised in the improved Alexnet to enhance feature extraction capability. Batch normalisation is used for preventing network overfitting while also improving the model’s robustness. The Adabound optimizer and the Relu activation function are used to improve convergence and accuracy. The facial image feature improvement approach is helpful to increasing the capability of the improved Alexnet in trials from many aspects. For face images acquired in the natural surroundings, our technique displays significant stability, serving as a benchmark for the intelligent prediction of other facial images.

Keywords

Facial Expression Recognition, Deep Learning, Convolutional Neural Network, Improved Alexnet
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  • F. Bourel, C.C. Chibelushi and A.A. Low, “Recognition of Facial Expressions in the Presence of Occlusion”, Proceedings of British Conference on Machine Vision, pp. 1-10, 2001.

  • P. Ekman, “Facial Expressions of Emotion: An Old Controversy and New Findings”, Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, Vol. 335, No. 1273, pp. 63-69, 1992.

  • H. Sikkandar and R. Thiyagarajan, “Deep Learning based Facial Expression Recognition using Improved Cat Swarm Optimization”, Journal of Ambient Intelligence and Humanized Computing, Vol. 12, No. 2, pp. 3037-3053, 2021.

  • H. Ali, M. Hariharan, S. Yaacob and A.H. Adom, “Facial Emotion Recognition using Empirical Mode Decomposition”, Expert Systems with Applications, Vol. 42, No. 3, pp. 1261-1277, 2015.

  • H. Wu, Y. Liu, Y. Liu and S. Liu, “Efficient Facial Expression Recognition via Convolution Neural Network and Infrared Imaging Technology”, Infrared Physics and Technology, Vol. 102, pp. 103031-103039, 2019.

  • Y. Tang, X. M. Zhang and H. Wang, “Geometric-Convolutional Feature Fusion based on Learning Propagation for Facial Expression Recognition”, IEEE Access, Vol. 6, pp. 42532-42540, 2018.

  • L. Ma, “Facial Expression Recognition using 2-D DCT of Binarized Edge Images and Constructive Feedforward Neural Networks”, Proceedings of IEEE International Joint Conference on Neural Networks, pp. 4083-4088, 2008.

  • S. Mohseni, H. M. Kordy and R. Ahmadi, “Facial Expression Recognition using DCT Features and Neural Network based Decision Tree”, Proceedings International Conference on Electronics in Marine, pp. 361-364, 2013.

  • H. Jung, S. Lee, J. Yim, S. Park and J. Kim, “Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition”, Proceedings of IEEE International Conference on Computer Vision, pp. 2983-2991, 2015.

  • A. Khan, A. Sohail, U. Zahoora and A.S. Qureshi, “A Survey of the Recent Architectures of Deep Convolutional Neural Networks”, Artificial Intelligence Review, Vol. 53 No. 8, pp. 5455-5516, 2020.

  • P. Liu, S. Han, Z. Meng and Y. Tong, “Facial Expression Recognition via a Boosted Deep Belief Network”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1805-1812, 2014.

  • P. Burkert, F. Trier, M.Z. Afzal, A. Dengel and M. Liwicki, “Dexpression: Deep Convolutional Neural Network for Expression Recognition”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1110-1117, 2015.

  • M. Liu, S. Li, S. Shan and X. Chen, “Au-Inspired Deep Networks for Facial Expression Feature Learning”, Neurocomputing, Vol. 159, pp. 126-136, 2015.

  • P. Liu, S. Han, Z. Meng and Y. Tong, “Facial Expression Recognition via a Boosted Deep Belief Network”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1805-1812, 2014.

  • M. Lyons, S. Akamatsu, M. Kamachi and J. Gyoba, “Coding Facial Expressions with Gabor Wavelets”, Proceedings of 3rd IEEE International Conference on Automatic Face and Gesture Recognition, pp. 200-205,1998.

  • P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar and I. Matthews, “The Extended Cohn-Kanade Dataset (ck+): A Complete Dataset for Action Unit and Emotion-Specified Expression”, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, pp. 94-101, 2010.

  • I. Song, H.J. Kim and P.B. Jeon, “Deep Learning for Real-Time Robust Facial Expression Recognition on a Smartphone”, Proceedings of IEEE International Conference on Consumer Electronics, pp. 564-567, 2014.

  • N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”, Journal of Machine Learning Research, Vol. 15, No. 1, pp. 1929-1958, 2014.

  • L.B. Krithika and G.L. Priya, “Graph based Feature Extraction and Hybrid Classification Approach for Facial Expression Recognition”, Journal of Ambient Intelligence and Humanized Computing, Vol. 12, No. 2, pp. 2131-2147, 2021.

  • S.C. Tai and K.C. Chung, “Automatic Facial Expression Recognition System using Neural Networks”, Proceedings of International Conference on TENCON, pp. 1-4, 2007.

  • A.N. Sreevatsan, K.S. Kumar, S. Rakeshsharma and R. Mansoor, “Emotion Recognition from Facial Expressions: A Target Oriented Approach using Neural Network”, Proceedings of International Conference on Machine Learning, pp. 497-502, 2004.

  • A. Mollahosseini, D. Chan and M.H. Mahoor, “Going Deeper in Facial Expression Recognition using Deep Neural Networks”, Proceedings of International Conference on Applications of Computer Vision, pp. 1-10, 2016.

  • R. Walecki, O. Rudovic, V. Pavlovic, B. Schuller and M. Pantic, “Deep Structured Learning for Facial Expression Intensity Estimation”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 5709-5718, 2017.

  • G. Yolcu, I. Oztel, S. Kazan, C. Oz and F. Bunyak, “Deep Learning-Based Face Analysis System for Monitoring Customer Interest”, Journal of Ambient Intelligence and Humanized Computing, Vol. 11, No. 1, pp. 237-248, 2020.

  • K. Zhao, W. S. Chu and H. Zhang, “Deep Region and Multi-Label Learning for Facial Action Unit Detection”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3391-3399, 2016.

  • H. Yang, U. Ciftci and L. Yin, “Facial Expression Recognition by De-Expression Residue Learning”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2168-2177, 2018.

  • S. Minaee, M. Minaei and A. Abdolrashidi, “Deep-Emotion: Facial Expression Recognition using Attentional Convolutional Network”, Sensors, Vol. 21, No. 9, pp. 3046-3056, 2021.

  • K. Wang, X. Peng, J. Yang, D. Meng and Y. Qiao, “Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition”, IEEE Transactions on Image Processing, Vol. 29, pp. 4057-4069, 2020.

  • Y. Wen, K. Zhang, Z. Li and Y. Qiao, “A Discriminative Feature Learning Approach for Deep Face Recognition”, Proceedings of International Conference on computer Vision, pp. 499-515, 2016.

  • K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition”, Proceedings of International Conference on Computer Vision, pp. 1-14, 2014.

  • K. He, X. Zhang, S. Ren and J. Sun, “Deep Residual Learning for Image Recognition”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.

  • C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov and A. Rabinovich, “Going Deeper with Convolutions”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9, 2015.

  • V. Kazemi and J. Sullivan, “One Millisecond Face Alignment with an Ensemble of Regression Trees”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1867-1874, 2014.

  • R. Ranjan, V.M. Patel and R. Chellappa, “Hyperface: A Deep Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation and Gender Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 41, No. 1, pp. 121-135, 2017.

  • J. Peters, D. Janzing and B. Scholkopf, “Identifying Cause and Effect on Discrete Data using Additive Noise Models”, Proceedings of International Conference on Artificial Intelligence and Statistics, pp. 597-604, 2010.

  • A. Krizhevsky, I. Sutskever and G.E. Hinton, “Imagenet Classification with Deep Convolutional Neural Networks”, Advances in Neural Information Processing Systems, Vol. 25, pp. 1097-1105, 2012.

  • A. Botev, G. Lever and D. Barber, “Nesterov’s Accelerated Gradient and Momentum as Approximations to Regularised Update Descent”, Proceedings of International Conference on Neural Networks, pp. 1899-1903, 2017.

  • A.C. Wilson, R. Roelofs, M. Stern, N. Srebro and B. Recht, “The Marginal Value of Adaptive Gradient Methods in Machine Learning”, Proceedings of International Conference on Neural Networks, pp. 1707-1714, 2017.

  • N.S. Keskar and R. Socher, “Improving Generalization Performance by Switching from Adam to Sgd”, Proceedings of International Conference on Neural Networks, pp. 1508-1514, 2017.

  • L. Luo, Y. Xiong, Y. Liu and X. Sun, “Adaptive Gradient Methods with Dynamic Bound of Learning Rate”, Proceedings of International Conference on Neural Networks, pp. 988-996, 2019.


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  • Facial Expression Recognition based on Feature Enhancement and Improved Alexnet

Abstract Views: 242  |  PDF Views: 99

Authors

Himanshukumar D. Nayak
Department of Electronics and Communication Engineering, Gujarat Technological University, India, India
Ashish K. Sarvaiya
Department of Electronics and Communication Engineering, Government Engineering College, Bhavnagar, India, India

Abstract


For interpersonal relations among humans, facial expressions are extremely important. Due to the complications in collecting required features from the frequently changing surroundings, uneven reflection from light sources, and many other aspects, facial expression recognition will encounter significant problems. A novel facial image recognition approach is proposed in this paper. Initially, a face image enhancement framework is created that is capable of enhancing the features of a face in a complicated context for this strategy. The improved Alexnet neural network is then created, which is based on the Alexnet architecture. Multi-scale convolution process is utilised in the improved Alexnet to enhance feature extraction capability. Batch normalisation is used for preventing network overfitting while also improving the model’s robustness. The Adabound optimizer and the Relu activation function are used to improve convergence and accuracy. The facial image feature improvement approach is helpful to increasing the capability of the improved Alexnet in trials from many aspects. For face images acquired in the natural surroundings, our technique displays significant stability, serving as a benchmark for the intelligent prediction of other facial images.

Keywords


Facial Expression Recognition, Deep Learning, Convolutional Neural Network, Improved Alexnet

References