Transforming Facial Expression Prediction: Amplifying Accuracy with ResNet50 Features and Innovated XG-Boost Algorithm
Main Article Content
Abstract
Facial expression is one of the most natural and a non-verbal way in expressing human emotions and interactions. Assessing the well-being via communication are the first signs that transmits the emotional state. This domain attracts more of research and are interested by the modality in the specificity of the domain. With a suspect able increase in the AI in domains, gaining a reasonable implementation of DL with its advanced ability in case of Facial Expression Detection known as FER. Existing studies adapting ML approaches, in the FER, have deployed in adequate laybacks such as high computational time, inability to deal with large datasets, and fail in bringing timely accurate ranges of predictions. In consideration to these aspects, the proposed study admits the system design, in aim of FER comprising feature extraction and the classification of the emotions using the DL models. These are done using the proposed approach uses Deep Multi-level feature extraction using ResNet50, which is more appropriate in optimal and exact feature selection mechanism. Followed by Weight-normalized XG-Boost classifier for the process of classifying various emotional expressions. This is adapted in aim, of maintaining the gradient descent step and admitting using larger dataset for learning. The input images are collected from the FER13, dataset consisting 28,709 sample image data and the test data consists of about 3,589 image data. These are initially pre-processed for better accuracy rates during feature extraction and classifications. The complete model in effective FER is evaluated using the performance metrics comprising Accuracy, Recall. F1-score and the precision rates. This analysis of the performance will aid in affirming the overall efficacy of the proposed system.
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References
B. Catanzariti, "Seeing affect: knowledge infrastructures in facial expression recognition systems," 2023.
G. Bargshady, X. Zhou, R. C. Deo, J. Soar, F. Whittaker, and H. Wang, "Enhanced deep learning algorithm development to detect pain intensity from facial expression images," Expert Systems with Applications, vol. 149, p. 113305, 2020.
J. Chen, Y. Lv, R. Xu, and C. Xu, "Automatic social signal analysis: Facial expression recognition using difference convolution neural network," Journal of Parallel and Distributed Computing, vol. 131, pp. 97-102, 2019.
S. Zhang, X. Pan, Y. Cui, X. Zhao, and L. Liu, "Learning affective video features for facial expression recognition via hybrid deep learning," IEEE Access, vol. 7, pp. 32297-32304, 2019.
A. Khattak, M. Z. Asghar, M. Ali, and U. Batool, "An efficient deep learning technique for facial emotion recognition," Multimedia Tools and Applications, pp. 1-35, 2022.
M. Aamir, Z. Rahman, W. A. Abro, U. A. Bhatti, Z. A. Dayo, and M. Ishfaq, "Brain tumor classification utilizing deep features derived from high-quality regions in MRI images," Biomedical Signal Processing and Control, vol. 85, p. 104988, 2023.
N. Andresen, M. Wöllhaf, K. Hohlbaum, L. Lewejohann, O. Hellwich, C. Thöne-Reineke, et al., "Towards a fully automated surveillance of well-being status in laboratory mice using deep learning: Starting with facial expression analysis," PLoS One, vol. 15, p. e0228059, 2020.
M. Flynn, D. Effraimidis, A. Angelopoulou, E. Kapetanios, D. Williams, J. Hemanth, et al., "Assessing the effectiveness of automated emotion recognition in adults and children for clinical investigation," Frontiers in human neuroscience, vol. 14, p. 70, 2020.
C. Pabba and P. Kumar, "An intelligent system for monitoring students' engagement in large classroom teaching through facial expression recognition," Expert Systems, vol. 39, p. e12839, 2022.
N. Andresen, M. Wöllhaf, K. Hohlbaum, L. Lewejohann, O. Hellwich, C. Thöne-Reineke, et al., "Towards a fully automated surveillance of well-being status in laboratory mice using deep learning," BioRxiv, p. 582817, 2019.
Y. Xie, W. Tian, H. Zhang, and T. Ma, "Facial expression recognition through multi-level features extraction and fusion," Soft Computing, pp. 1-16, 2023.
M. Karnati, A. Seal, D. Bhattacharjee, A. Yazidi, and O. Krejcar, "Understanding deep learning techniques for recognition of human emotions using facial expressions: a comprehensive survey," IEEE Transactions on Instrumentation and Measurement, 2023.
T. Shahzad, K. Iqbal, M. A. Khan, and N. Iqbal, "Role of zoning in facial expression using deep learning," IEEE Access, vol. 11, pp. 16493-16508, 2023.
P. Barros, N. Churamani, and A. Sciutti, "The facechannel: a fast and furious deep neural network for facial expression recognition," SN Computer Science, vol. 1, pp. 1-10, 2020.
Z. Zhao, Q. Liu, and S. Wang, "Learning deep global multi-scale and local attention features for facial expression recognition in the wild," IEEE Transactions on Image Processing, vol. 30, pp. 6544-6556, 2021.
R. K. Mishra, S. Urolagin, J. A. A. Jothi, and P. Gaur, "Deep hybrid learning for facial expression binary classifications and predictions," Image and Vision Computing, vol. 128, p. 104573, 2022.
C. Benegui and R. T. Ionescu, "Convolutional neural networks for user identification based on motion sensors represented as images," IEEE Access, vol. 8, pp. 61255-61266, 2020.
L. Wang, Z. He, B. Meng, K. Liu, Q. Dou, and X. Yang, "Two-pathway attention network for real-time facial expression recognition," Journal of Real-Time Image Processing, vol. 18, pp. 1173-1182, 2021.
A. H. Farzaneh and X. Qi, "Facial Expression Recognition in the Wild via Deep Attentive Center Loss."
M. Egger, M. Ley, and S. Hanke, "Emotion recognition from physiological signal analysis: A review," Electronic Notes in Theoretical Computer Science, vol. 343, pp. 35-55, 2019.
A. T. Wasi, K. Šerbetar, R. Islam, T. H. Rafi, and D.-K. Chae, "ARBEx: Attentive feature extraction with reliability balancing for robust facial expression learning," arXiv preprint arXiv:2305.01486, 2023.
R. Banerjee, S. De, and S. Dey, "A survey on various Deep Learning algorithms for an efficient facial expression recognition system," International Journal of Image and Graphics, vol. 23, p. 2240005, 2023.
F. Wang, F. Xie, S. Shen, L. Huang, R. Sun, and J. Le Yang, "A novel multiface recognition method with short training time and lightweight based on abasnet and h-softmax," IEEE Access, vol. 8, pp. 175370-175384, 2020.
A. Hassouneh, A. Mutawa, and M. Murugappan, "Development of a real-time emotion recognition system using facial expressions and EEG based on machine learning and deep neural network methods," Informatics in Medicine Unlocked, vol. 20, p. 100372, 2020.
J. Zhang, Z. Yin, P. Chen, and S. Nichele, "Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review," Information Fusion, vol. 59, pp. 103-126, 2020.
A. Chowanda, "Separable convolutional neural networks for facial expressions recognition," Journal of Big Data, vol. 8, p. 132, 2021.
I. P. Adegun and H. B. Vadapalli, "Facial micro-expression recognition: A machine learning approach," Scientific African, vol. 8, p. e00465, 2020.
H. I. Dino and M. B. Abdulrazzaq, "Facial Expression Classification Based on SVM, KNN and MLP Classifiers."
M. Awais, X. Long, B. Yin, S. F. Abbasi, S. Akbarzadeh, C. Lu, et al., "A hybrid DCNN-SVM model for classifying neonatal sleep and wake states based on facial expressions in video," IEEE Journal of Biomedical and Health Informatics, vol. 25, pp. 1441-1449, 2021.
K. S. Yadav and J. Singha, "Facial expression recognition using modified Viola-John’s algorithm and KNN classifier," Multimedia Tools and Applications, vol. 79, pp. 13089-13107, 2020.
J. M. Song, W. Kim, and K. R. Park, "Finger-vein recognition based on deep DenseNet using composite image," Ieee Access, vol. 7, pp. 66845-66863, 2019.
M. Akhand, S. Roy, N. Siddique, M. A. S. Kamal, and T. Shimamura, "Facial emotion recognition using transfer learning in the deep CNN," Electronics, vol. 10, p. 1036, 2021.
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, pp. 5455-5516, 2020.
S. Liu, "Image Classification of Static Facial Expressions in the Wild Based on Bidirectional Neural Networks (Based on Pytorch)."