The International Arab Journal of Information Technology (IAJIT)


Pain Detection/Classification Framework including Face Recognition based on the Analysis of Facial

Facial expressions can demonstrate the presence and degree of pain of humans, which is a vital topic in E- healthcare domain specially for elderly people or patients with special needs. This paper presents a framework for pain detection, pain classification, and face recognition using feature extraction, feature selection, and classification techniques. Pain intensity is measured by Prkachin and Solomon pain intensity scale. Experimental results showed that the proposed framework is a promising one compared with previously works. It achieves 91% accuracy in pain detection, 99.89% accuracy in face recognition, and 78%, 92%, 88% accuracy, respectively, for three levels of pain classification.

[1] Ashraf A., Lucey S., Cohn J., Chen T., Ambadar Z., Prachin K., and Solomon P., “The Painful Face-Pain Expression Recognition Using Active Appearance Models,” Image and Vision Computing, vol. 27, no. 12, pp. 1788-1796, 2009.

[2] Bargshady G., Zhou X., Deo R., Soar J., Whittaker F., and Wang H., “Enhanced Deep Learning Algorithm Development to Detect Pain Intensity from Facial Expression Images,” Expert Systems with Applications, vol. 149, 2020.

[3] Bargshady G., Soar J., Zhou X., Deo R., Whittaker F., and Wang H., “A Joint Deep Neural Network Model for Pain Recognition from Face,” in Proceedings of IEEE 4th International Conference on Computer and Communication Systems, Singapore, pp. 52-56, 2019.

[4] Chehrehgosha A., Emadi M., Boda R., and Priyadarsini M., “Face Detection and Tracking Using KLT and Viola Jones,” ARPN Journal of Engineering and Applied Sciences, vol. 11, no. 23, pp. 13472- 13476, 2016.

[5] Capela N., Lemaire E., and Baddour N., “Feature Selection for Wearable Smartphone-based Human Activity Recognition with Able bodied, Elderly, and Stroke Patients,” PlosOne, vol. 10, no. 4, 2015.

[6] Chehrehgosha A. and Emadi M., “Face Detection Using Fusion of LBP and Adaboost,” Journal of Soft Computing and Applications, vol. 2016, pp. 1-10, 2016.

[7] Ghosh A., Datta A., and Ghosh S., “Self- Adaptive Differential Evolution for Feature Selection in Hyperspectral Image Data,” Application of Soft Computing Journal, vol. 13, no. 4, pp. 1969-1977, 2013.

[8] Freuend Y. and Schapire R., “Experiments with a New Boosting Algorithm,” in Proceedings of the 13th International Conference on Machine Learning, Bari, pp.148-156, 1996.

[9] Gupta A. and Goel L., “Heuristic Approach for Face Recognition using Artificial Bee Colony Optimization,” in Proceedings of the Intelligent System Technologies and Applications, Jaipur, pp. 209-223, 2016.

[10] Hammal Z. and Cohn J., “Automatic Detection of Pain Intensity,” in Proceedings of the 14th ACM International Conference on Multimedia Interaction, Santa Monica, pp. 47-52, 2012.

[11] Irani R., Nasollahi K., and Moeslund T., “Pain Recognition using Spatiotemporal Oriented Energy of Facial Muscles,” in Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Boston, pp. 80- 87, 2015.

[12] Khan R., Meyer A., Konik H., and Bouakaz S., “Pain Detection Through Shape and Appearance Features,” in Proceedings of the IEEE International Conference on Multimedia and Expo, San Jose, pp.1-6, 2013.

[13] Lajevardi S., “Structural Similarity Classifier for Facial Expression Recognition,” Signal, Image and Video Processing, vol. 8, pp. 1103-1110, 2014.

[14] Kononenko I., “Estimating Attributes: Analysis and Extensions of Relieff,” in Proceedings of the International Conference on Machine Learning, Catania, pp. 171-182, 1994.

[15] Kumar B., “Boosting Techniques in Rarity Mining,” International Journal of Advanced Research in Computer Science and Software Engineering, no. 2, pp. 27-35, 2012.

[16] Lucey P., Cohn J., Matthews I., Lucey S., Sridharan S., Howlett J., and Prkachin K., “Automatically Detecting Pain in Video Through Facial Action Units,” IEEE Transaction on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 41, no. 3, pp. 664-674, 2011.

[17] Lucey P., Cohn J., Prachin K., Solomon P., and Matthews I., “Painful Data: The UNBC- McMaster Shoulder Pain Expression Archive Database,” in Proceedings of the IEEE International Conference on Automatic Face Gesture Recognition and Workshops, Santa Barbara, pp. 57-64, 2011.

[18] Pedersen H., “Learning Appearance Features for Pain Detection Using the UNBC-McMaster Shoulder Pain Expression Archive Database,” in Proceedings of International Conference on Computer Vision Systems, Copenhagen, pp. 128- 136, 2015.

[19] Rathee N. and Ganotra D., “A Novel Approach for Pain Intensity Detection Based on Facial Feature Deformations,” Journal of Visual Communication and Image Representation, vol. 33, pp. 247-254, 2015. The International Arab Journal of Information Technology, Vol. 18, No. 1, January 2021 132

[20] Rodriguez P., Cucurull G., Gonz`alez J., Gonfaus J., Nasrollahi K., Moeslund T., and Roca F., “Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification,” IEEE Transactions on Cybernetics, no. 99, pp. 1-11, 2017.

[21] Satange D., Alsubari A., and Ramteke R., “Composite Feature Extraction based on Gabor and Zernike Moments for Face Recognition,” IOSR Journal of Computer Engineering, pp. 17- 23, 2017.

[22] Shier W. and Yanushkevich S., “Pain Recognition and Intensity Classification Using Facial Expression,” in Proceedings of The International Joint Conference on Neural Networks, Vancouver, pp. 3578-3583, 2016.

[23] Singh A. and Pandey B., “Diagnosis of Liver Disease Using Correlation Distance Metric Based K-Nearest Neighbor Approach,” in Proceedings of International Symposium on Intelligent Systems Technologies and Applications, Jaipur, pp. 845-856, 2016.

[24] Singh S., Tiwari S., Abidi A., and Singh A., “Prediction of Pain Intensity using Multimedia Data,” Multimedia Applications and Tools, Springer, vol. 76, no. 18, pp. 19317-19342, 2013.

[25] Skurichena M. and Duin R., “Bagging, Boosting and the Random Subspace Method for Linear Classifiers,” Pattern Analysis and Applications, Springer, vol. 5, no. 2, pp. 121-135, 2002.

[26] Storn R. and Price K., “Differential Evolution- a Simple and Efficient Heuristic for Global Optimization over Continuous Spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341- 359, 1997.

[27] Soni L., Datar A., and Datar S., “Viola-Jones Algorithm Based Approach for Face Detection of African Origin People and Newborn Infants,” International Journal of Computer Trends and Technology, vol. 51, no. 2, pp. 75-81, 2017.

[28] Tavakolian M. and Hadid A., “A Spatiotemporal Convolutional Neural Network for Automatic Pain Intensity Estimation from Facial Dynamics,” International Journal of Computer Vision, vol. 127, no. 10, pp. 1413-1425, 2019.

[29] Tuysuzoglu G. and Berant D., “Enhanced Bagging (eBagging): A Novel Approach for Ensemble Learning,” The International Arab journal of Information Technology, vol. 17, no. 4, pp. 515-528, 2019.

[30] Zafar Z. and Khan N., “Pain Intensity Evaluation through Facial Action Units,” in Proceedings of the 22nd International Conference on Pattern Recognition, Stockholm, pp. 4696-4701, 2014.

[31] Zainudin M., Sulaiman Md., Mustapha N., Perumal T., Nazre A., Mohamed R., and Abd Manaf S., “Feature Selection Optimization using Hybrid Relieff with Self-Adaptive Differential Evolution,” International Journal of Intelligent Engineering and Systems, vol. 10, no. 3, pp. 21- 29, 2017. Fatma Elgendy was born in Kafrelshiekh, Egypt, in 1983. She received the B. Sc and M. Sc in Computer Engineering from the Faculty of Engineering, Tanta University, in 2005, and 2014, respectively. She is working as Assistant lecturer in the Department of Computer Engineering and Automatic Control, Kafrelshiekh Higher institute for Engineering and Technology, Egypt. Her interests are in the area of: Image processing, Object Recognition, Cryptography, Healthcare, and IOT applications. Mahmoud Alshewimy was born in Tanta, Egypt, in 1977. He received the M.Sc. degree in computer engineering and automatic control from Tanta University (Egypt) in 2006 and Ph.D. from Istanbul University in 2014. He is working as Associate Professor in the Department of Computer Engineering and Automatic Control, Tanta University, Egypt. His research interests include Software/Hardware Co-design, Object Recognition & Image Processing, and IOT applications. Amany Sarhan, received the B.Sc degree in Electronics Engineering, and M.Sc. in Computer Engineering from the Faculty of Engineering, Mansoura University, in 1990, and 1997, respectively. She awarded the Ph.D. degree as a joint research between Tanta Univ., Egypt and Univ. of Connecticut, USA. She is working now as a Full Prof. and head Computers and Control Dept., Tanta Univ., Egypt. Her interests are in the area of: Network, Distributed Systems, Image and video processing, GPU and Distributed Computations.