An Improved Facial Expression Recognition Method Using Combined Hog and Gabor Features
DOI:
https://doi.org/10.25271/sjuoz.2022.10.2.897Keywords:
facial expression recognition, HOG features, GABOR feature, FER2013 dataset, SVMAbstract
Lately, face recognition technology has been a significant study and a topic for generations. It remains a difficult task because of the variability of wide interclass. The subject of facial expression recognition is addressed in this research using a practical method. This method can recognize the human face and it is various features such as the eyes, brows, and lips. The motions or deformations of the face muscles are the cause of facial expressions. In addition, computer vision tasks such as texture recognition and categorization are commonly used. Furthermore, feature extraction basically discovers groups of features that demonstrate an image of visual texture. It is a critical phase to complete the operation. This work extracts features utilizing Histogram of Oriented Gradients (HOG) and Gabor approaches and then combines extracted features to improve the accuracy of facial expression detection. The derived features were particularly sensitive to object deformations. Later on, the classification of facial expression is handled using (Support Vector Machine) SVM. Analyze the proposed approach on FER 2013 data to see how well it performs. The proposal has a categorization rate of 63.82% on average. The proposed technique determines the comparable classification accuracy as shown in experimental findings. To improve this work it is planned to use deep features and combined them with HOG or Gabor, as well as to show the efficiency of the work it can be implemented with more datasets such as the JAFFE database.
References
Abrahim A. R., Shafry M. Rahim M., & Sulong, G. (2019). Splicing image forgery identification based on artificial neural network approach and texture features, Cluster Computing, vol. 22, no. Supplement 1, p. 647–660.
Ashir, A. M., Eleyan, A., & Akdemir, B. (2020). Facial expression recognition with dynamic cascaded classifier. Neural Com- puting and Applications, vol. 32, no. 10, pp. 6295–6309.
Bakchy, S. C., Ferdous, M. J., Sathi, A. H., Ray, K. C., Imran, F., & Ali, M. M. (2017). Facial expression recognition based on support vector machine using Gabor wavelet filter. In 2017 2nd International Conference on Electrical & Electronic Engineering (ICEEE) (pp. 1-4). IEEE.
Bougourzi, F., Mokrani, K., Ruichek, Y., Dornaika, F., Ouafi, A., & Taleb-Ahmed, A. (2019). Fusion of transformed shallow features for facial expression recognition. IET Image Processing, vol. 13, no. 9, pp. 1479–1489.
Chen, J., Chen, Z., Chi, Z., & Fu, H. (2014). Facial expression recognition based on facial components detection and hog features. In International workshops on electrical and computer engineering subfields, pp. 884-888.
Dalal, N., & Triggs, B. (2005). Histograms of Oriented Gradients for Human Detection, in Computer Vision and Pattern Recognition. IEEE Conference on, 2005, pp. 886-893.
Ekman, P., Friesen, W.V. (1971). Constants across cultures in the face and emotion. Journal of Pers. Soc. Psychol., 17(2), pp. 124–129
Feng, X., Hadid, A., & Pietikäinen, M. (2004). A coarse-to-fine classification scheme for facial expression recognition. In Image Analysis and Recognition, pp. 668-675.
Hassan, M. M., Hussein, H. I., Eesa, A. S., & Mstafan, R. J. (2021). Face Recognition Based on Gabor Feature Extraction Followed by FastICA and LDA. CMC-COMPUTERS MATERIALS & CONTINUA, 68(2), 1637-1659.
Ibrahim, D. A., Zebari, D. A., Ahmed, F. Y., & Zeebaree, D. Q. (2021). Facial Expression Recognition Using Aggregated Handcrafted Descriptors based Appearance Method. In 2021 IEEE 11th International Conference on System Engineering and Technology (ICSET) (pp. 177-182). IEEE.
Khandait, S., Thool, R., & Khandait, P. (2012). Automatic facial feature extraction and expression recognition based on neural network. International Journal of Advanced Computer Science and Applications, vol. 2, pp. 113-118.
Kumar, P., Happy, S L, & Routray, A. (2016). A Real-time Robust Facial Expression Recognition System using HOG Features. International Conference on Computing, Analytics and Security Trends (CAST), pp. 289-293.
Łabędź, P., Skabek, K., Ozimek, P., & Nytko, M. (2021). Histogram Adjustment of Images for Improving Photogrammetric Reconstruction. Sensors, 21(14), 4654.
Lekdioui, K., Ruichek, Y., Messoussi, R., Chaabi, Y., & Touahni, R. (2017). Facial expression recognition using face-regions. International conference on advanced technologies for signal and image processing (ATSIP) (pp. 1-6). IEEE.
Liu, K., Zhang, M., & Pan, Z. (2016). Facial expression recognition with CNN ensemble, in: Proceeding of the International Conference on Cyberworlds (CW), IEEE, pp. 163–166.
Liu, Y., Li, Y., Ma, X., & Song, R. (2017). Facial expression recognition with fusion features extracted from salient facial areas. Sensors, vol. 17, no. 4, p. 712.
Mahmud, F., & Al Mamun, Md. (2017). Facial expression recognition system using extreme learning machine. International Journal of Scientific & Engineering Research, 8, (3), pp. 26–30.
Majumder, A., Behera, L., & Subramanian, V.K. (2018). Automatic facial expression recognition system using deep network-based data fusion. IEEE Trans on Cybern. 48 (1) 103–114
Mandal, J. K., & Mukhopadhyay, S. (2013). Adaptive Median Filtering Based on Unsupervised Classification of Pixels. In Handbook of Research on Computational Intelligence for Engineering, Science, and Business (pp. 273-296). IGI Global.
Meena, H. K., Sharma, K. K., & Joshi, S. D. (2020). Effective cur- velet-based facial expression recognition using graph signal processing. Signal, Image and Video Processing, vol. 14, no. 2, pp. 241–247.
Mollahosseini, A., Chan, D., & Mahoor, M.H. (2016). Going deeper in facial expression recognition using deep neural networks. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA, 7–10; pp. 1–10.
Nigam, S., Singh, R., & Misra, A. K. (2018). Efficient facial ex- pression recognition using histogram of oriented gradients in wavelet domain. Multimedia Tools and Applications, vol. 77, no. 21, pp. 28725–28747.
Niu, B., Gao, Z., & Guo, B. (2021). Facial expression recognition with LBP and ORB features. Computational Intelligence and Neuroscience.
Qi, C., Li, M., Wang, Q., Zhang, H., Xing, J., Gao, Z., & Zhang, H. (2018). Facial expressions recognition based on cognition and mapped binary patterns. IEEE Access, 6, 18795-18803.
Ryu, B., Rivera, A. R., Kim, J., & Chae, O. (2017). Local directional ternary pattern for facial expression recognition. IEEE Transactions on Image Processing, vol. 26, no. 12, pp. 6006– 6018.
Shah, S. K., & Khanna, V. (2015). Facial expression recognition for color images using Gabor, log Gabor filters and PCA. International Journal of Computer Applications, 113(4).
Shan, C., Gong, S., & McOwan, P. W. (2009). Facial expression recognition based on Local Binary Patterns: A comprehensive study. Image and Vision Computing, vol. 27, pp. 803-816.
Taher, K. I., Abdulazeez, A. M., & Zebari, D. A. (2021). Data Mining Classification Algorithms for Analyzing Soil Data. Asian Journal of Research in Computer Science, 17-28.
Tsai, H.-H., & Chang, Y.-C. (2018). Facial expression recognition using a combination of multiple facial features and support vector machine. Soft Computing, vol. 22, no. 13, pp. 4389–4405.
Turan, C., & Lam, K.-M. (2018). Histogram-based local descriptors for facial expression recognition (FER): a comprehensive study. Journal of Visual Communication and Image Repre- sentation, vol. 55, pp. 331–341.
Xiang, J., & Zhu, G. (2017). Joint face detection and facial expression recognition with MTCNN, in: Proceedings of the 4th International Conference on Information Science and Control Engineering (ICISCE), IEEE, pp. 424–427.
Zebari, D. A., Abrahim, A. R., Ibrahim, D. A., Othman, G. M., & Ahmed, F. Y. (2021). Analysis of Dense Descriptors in 3D Face Recognition. In 2021 IEEE 11th International Conference on System Engineering and Technology (ICSET) (pp. 171-176). IEEE.
Zebari, D. A., Haron, H., Zeebaree, S. R., & Zeebaree, D. Q. (2019). Enhance the mammogram images for both segmentation and feature extraction using wavelet transform. In 2019 International Conference on Advanced Science and Engineering (ICOASE) (pp. 100-105). IEEE.
Zebari, D. A., Zeebaree, D. Q., Abdulazeez, A. M., Haron, H., & Hamed, H. N. A. (2020). Improved threshold based and trainable fully automated segmentation for breast cancer boundary and pectoral muscle in mammogram images. IEEE Access, 8, 203097-203116.
Zebari, G. M., Zebari, D. A., Zeebaree, D. Q., Haron, H., Abdulazeez, A. M., & Yurtkan, K. (2021). Efficient CNN Approach for Facial Expression Recognition. In Journal of Physics: Conference Series (Vol. 2129, No. 1, p. 012083). IOP Publishing.
Zeng, G., Zhou, J., Jia, X., Xie, W., & Shen, L. (2018). Hand-crafted feature guided deep learning for facial expression recognition, in: Proceedings of the 2018 13th IEEE International Conference on Automatic Face Gesture Recognition (FG 2018), pp. 423–430.
Zhang, L., & Tjondronegoro, D. (2011). Facial expression recognition using facial movement features. Affective Computing, IEEE Transactions on, vol. 2, pp. 219-229.
Ashir, A. M., Eleyan, A., & Akdemir, B. (2020). Facial expression recognition with dynamic cascaded classifier. Neural Com- puting and Applications, vol. 32, no. 10, pp. 6295–6309.
Bakchy, S. C., Ferdous, M. J., Sathi, A. H., Ray, K. C., Imran, F., & Ali, M. M. (2017). Facial expression recognition based on support vector machine using Gabor wavelet filter. In 2017 2nd International Conference on Electrical & Electronic Engineering (ICEEE) (pp. 1-4). IEEE.
Bougourzi, F., Mokrani, K., Ruichek, Y., Dornaika, F., Ouafi, A., & Taleb-Ahmed, A. (2019). Fusion of transformed shallow features for facial expression recognition. IET Image Processing, vol. 13, no. 9, pp. 1479–1489.
Chen, J., Chen, Z., Chi, Z., & Fu, H. (2014). Facial expression recognition based on facial components detection and hog features. In International workshops on electrical and computer engineering subfields, pp. 884-888.
Dalal, N., & Triggs, B. (2005). Histograms of Oriented Gradients for Human Detection, in Computer Vision and Pattern Recognition. IEEE Conference on, 2005, pp. 886-893.
Ekman, P., Friesen, W.V. (1971). Constants across cultures in the face and emotion. Journal of Pers. Soc. Psychol., 17(2), pp. 124–129
Feng, X., Hadid, A., & Pietikäinen, M. (2004). A coarse-to-fine classification scheme for facial expression recognition. In Image Analysis and Recognition, pp. 668-675.
Hassan, M. M., Hussein, H. I., Eesa, A. S., & Mstafan, R. J. (2021). Face Recognition Based on Gabor Feature Extraction Followed by FastICA and LDA. CMC-COMPUTERS MATERIALS & CONTINUA, 68(2), 1637-1659.
Ibrahim, D. A., Zebari, D. A., Ahmed, F. Y., & Zeebaree, D. Q. (2021). Facial Expression Recognition Using Aggregated Handcrafted Descriptors based Appearance Method. In 2021 IEEE 11th International Conference on System Engineering and Technology (ICSET) (pp. 177-182). IEEE.
Khandait, S., Thool, R., & Khandait, P. (2012). Automatic facial feature extraction and expression recognition based on neural network. International Journal of Advanced Computer Science and Applications, vol. 2, pp. 113-118.
Kumar, P., Happy, S L, & Routray, A. (2016). A Real-time Robust Facial Expression Recognition System using HOG Features. International Conference on Computing, Analytics and Security Trends (CAST), pp. 289-293.
Łabędź, P., Skabek, K., Ozimek, P., & Nytko, M. (2021). Histogram Adjustment of Images for Improving Photogrammetric Reconstruction. Sensors, 21(14), 4654.
Lekdioui, K., Ruichek, Y., Messoussi, R., Chaabi, Y., & Touahni, R. (2017). Facial expression recognition using face-regions. International conference on advanced technologies for signal and image processing (ATSIP) (pp. 1-6). IEEE.
Liu, K., Zhang, M., & Pan, Z. (2016). Facial expression recognition with CNN ensemble, in: Proceeding of the International Conference on Cyberworlds (CW), IEEE, pp. 163–166.
Liu, Y., Li, Y., Ma, X., & Song, R. (2017). Facial expression recognition with fusion features extracted from salient facial areas. Sensors, vol. 17, no. 4, p. 712.
Mahmud, F., & Al Mamun, Md. (2017). Facial expression recognition system using extreme learning machine. International Journal of Scientific & Engineering Research, 8, (3), pp. 26–30.
Majumder, A., Behera, L., & Subramanian, V.K. (2018). Automatic facial expression recognition system using deep network-based data fusion. IEEE Trans on Cybern. 48 (1) 103–114
Mandal, J. K., & Mukhopadhyay, S. (2013). Adaptive Median Filtering Based on Unsupervised Classification of Pixels. In Handbook of Research on Computational Intelligence for Engineering, Science, and Business (pp. 273-296). IGI Global.
Meena, H. K., Sharma, K. K., & Joshi, S. D. (2020). Effective cur- velet-based facial expression recognition using graph signal processing. Signal, Image and Video Processing, vol. 14, no. 2, pp. 241–247.
Mollahosseini, A., Chan, D., & Mahoor, M.H. (2016). Going deeper in facial expression recognition using deep neural networks. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA, 7–10; pp. 1–10.
Nigam, S., Singh, R., & Misra, A. K. (2018). Efficient facial ex- pression recognition using histogram of oriented gradients in wavelet domain. Multimedia Tools and Applications, vol. 77, no. 21, pp. 28725–28747.
Niu, B., Gao, Z., & Guo, B. (2021). Facial expression recognition with LBP and ORB features. Computational Intelligence and Neuroscience.
Qi, C., Li, M., Wang, Q., Zhang, H., Xing, J., Gao, Z., & Zhang, H. (2018). Facial expressions recognition based on cognition and mapped binary patterns. IEEE Access, 6, 18795-18803.
Ryu, B., Rivera, A. R., Kim, J., & Chae, O. (2017). Local directional ternary pattern for facial expression recognition. IEEE Transactions on Image Processing, vol. 26, no. 12, pp. 6006– 6018.
Shah, S. K., & Khanna, V. (2015). Facial expression recognition for color images using Gabor, log Gabor filters and PCA. International Journal of Computer Applications, 113(4).
Shan, C., Gong, S., & McOwan, P. W. (2009). Facial expression recognition based on Local Binary Patterns: A comprehensive study. Image and Vision Computing, vol. 27, pp. 803-816.
Taher, K. I., Abdulazeez, A. M., & Zebari, D. A. (2021). Data Mining Classification Algorithms for Analyzing Soil Data. Asian Journal of Research in Computer Science, 17-28.
Tsai, H.-H., & Chang, Y.-C. (2018). Facial expression recognition using a combination of multiple facial features and support vector machine. Soft Computing, vol. 22, no. 13, pp. 4389–4405.
Turan, C., & Lam, K.-M. (2018). Histogram-based local descriptors for facial expression recognition (FER): a comprehensive study. Journal of Visual Communication and Image Repre- sentation, vol. 55, pp. 331–341.
Xiang, J., & Zhu, G. (2017). Joint face detection and facial expression recognition with MTCNN, in: Proceedings of the 4th International Conference on Information Science and Control Engineering (ICISCE), IEEE, pp. 424–427.
Zebari, D. A., Abrahim, A. R., Ibrahim, D. A., Othman, G. M., & Ahmed, F. Y. (2021). Analysis of Dense Descriptors in 3D Face Recognition. In 2021 IEEE 11th International Conference on System Engineering and Technology (ICSET) (pp. 171-176). IEEE.
Zebari, D. A., Haron, H., Zeebaree, S. R., & Zeebaree, D. Q. (2019). Enhance the mammogram images for both segmentation and feature extraction using wavelet transform. In 2019 International Conference on Advanced Science and Engineering (ICOASE) (pp. 100-105). IEEE.
Zebari, D. A., Zeebaree, D. Q., Abdulazeez, A. M., Haron, H., & Hamed, H. N. A. (2020). Improved threshold based and trainable fully automated segmentation for breast cancer boundary and pectoral muscle in mammogram images. IEEE Access, 8, 203097-203116.
Zebari, G. M., Zebari, D. A., Zeebaree, D. Q., Haron, H., Abdulazeez, A. M., & Yurtkan, K. (2021). Efficient CNN Approach for Facial Expression Recognition. In Journal of Physics: Conference Series (Vol. 2129, No. 1, p. 012083). IOP Publishing.
Zeng, G., Zhou, J., Jia, X., Xie, W., & Shen, L. (2018). Hand-crafted feature guided deep learning for facial expression recognition, in: Proceedings of the 2018 13th IEEE International Conference on Automatic Face Gesture Recognition (FG 2018), pp. 423–430.
Zhang, L., & Tjondronegoro, D. (2011). Facial expression recognition using facial movement features. Affective Computing, IEEE Transactions on, vol. 2, pp. 219-229.
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2022-06-07
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Hasan, Z. F. (2022). An Improved Facial Expression Recognition Method Using Combined Hog and Gabor Features. Science Journal of University of Zakho, 10(2), 54–59. https://doi.org/10.25271/sjuoz.2022.10.2.897
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